AI Research Papers Chatbot 🤖

App Link :- https://ragchatbotbuildwithai.streamlit.app/ An intelligent chatbot powered by Groq LLM that can answer questions about fundamental AI research papers, including GANs, Transformers, and Autoencoders.

🎯 Features

• Interactive chat interface built with Streamlit
• Powered by Groq's llama-3.3-70b model for high-quality responses
• Semantic search using HuggingFace embeddings
• Vector storage with ChromaDB for efficient retrieval
• Supports multiple research papers simultaneously
• Easy-to-use web interface
• Real-time response generation

📚 Included Research Papers

The chatbot is currently trained on these seminal AI papers:

• Generative Adversarial Nets (1406.2661v1)
• Attention Is All You Need (1706.03762v7)
• Autoencoders (2003.05991v2)

🛠️ Installation

1. Clone the repository:

https://github.com/Aryan-coder-student/BuildWithAI.git

2. Install required dependencies:

pip install -r requirements.txt

3. Create a .env file in the root directory and add your Groq API key:

GROQ_API_KEY=your_api_key_here

4. Place your PDF papers in the Papers directory.

🚀 Usage

1. Start the application:

streamlit run app.py

2. Open your browser and navigate to http://localhost:8501

3. Start asking questions about the research papers!

🔧 Technical Architecture

• LLM: Groq's llama-3.3-70b model for generating responses
• Embeddings: HuggingFace's sentence-transformers/all-MiniLM-L6-v2
• Vector Store: ChromaDB for efficient document retrieval
• Frontend: Streamlit for the web interface
• Document Processing: LangChain for PDF processing and chunking

📝 Environment Variables

The following environment variables are required:

GROQ_API_KEY=your_groq_api_key

🔄 Reset Vector Store

You can reset the vector store anytime using the "Reset Vector Store" button in the sidebar. This will:

• Delete the existing ChromaDB database
• Reinitialize the system with fresh embeddings
• Rebuild the vector store from scratch

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

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

🙏 Acknowledgments

• The Groq team for providing the LLM API
• LangChain for the document processing framework
• Streamlit for the web application framework
• The authors of the original research papers

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Made with ❤️ by [Aryan Pahari]