https://github.com/aryan-coder-student/buildwithai
An intelligent chatbot powered by Groq LLM that can answer questions about fundamental AI research papers, including GANs, Transformers, and Autoencoders.
https://github.com/aryan-coder-student/buildwithai
Last synced: 12 months ago
JSON representation
An intelligent chatbot powered by Groq LLM that can answer questions about fundamental AI research papers, including GANs, Transformers, and Autoencoders.
- Host: GitHub
- URL: https://github.com/aryan-coder-student/buildwithai
- Owner: Aryan-coder-student
- Created: 2025-01-11T17:08:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-15T04:55:19.000Z (about 1 year ago)
- Last Synced: 2025-06-15T05:44:59.321Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 52.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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:
```bash
https://github.com/Aryan-coder-student/BuildWithAI.git
```
2. Install required dependencies:
```bash
pip install -r requirements.txt
```
3. Create a `.env` file in the root directory and add your Groq API key:
```env
GROQ_API_KEY=your_api_key_here
```
4. Place your PDF papers in the `Papers` directory.
## 🚀 Usage
1. Start the application:
```bash
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:
```env
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](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
---
Made with ❤️ by [Aryan Pahari]