A Streamlit application that helps users research Bitcoin Layer 2 (L2) scaling solutions using RAG (Retrieval Augmented Generation).

The application has been split into multiple components for better maintainability:

├── main.py                # Entry point that runs the application
├── app.py                 # Main BitcoinL2RAG class
├── components/
│   ├── __init__.py        # Makes components a proper package
│   ├── ui.py              # UI components and styling
│   ├── rag.py             # RAG components (vector store, embeddings, etc.)
│   └── web_crawler.py     # Web crawling functionality
├── utils.py               # Utility functions
├── requirements.txt       # Dependencies
└── README.md              # This file

• Python 3.8+
• OpenRouter API key for the LLM
• Ollama running locally with nomic-embed-text model for embeddings

1. Clone this repository:

git clone <repository-url>
cd <repository-directory>

2. Install the required dependencies:

pip install -r requirements.txt

3. Create a .env file in the root directory with your OpenRouter API key:

OPENROUTER_API_KEY=your-openrouter-api-key
YOUR_SITE_URL=localhost  # Optional but recommended
YOUR_SITE_NAME=Bitcoin L2 Research Assistant  # Optional but recommended
USER_AGENT=BitcoinL2ResearchAssistant/1.0  # Recommended to identify your requests

4. Ensure Ollama is running locally with the required model:

ollama pull nomic-embed-text

Run the application using Streamlit:

streamlit run main.py

The application will be available at http://localhost:8501.

Place a CSV file named data.csv in the root directory containing Bitcoin L2 information. The application will automatically extract URLs from the CSV and crawl them for additional information.

• Interactive Q&A about Bitcoin L2 solutions
• Automatic web crawling from URLs found in CSV data
• Semantic search across both structured CSV data and unstructured web content
• Detailed citations and source tracking
• Sample questions for easy exploration