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| title: F1-AI | |
| emoji: 🏎️ | |
| colorFrom: red | |
| colorTo: gray | |
| sdk: streamlit | |
| sdk_version: "1.27.2" | |
| app_file: app.py | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # F1-AI: Formula 1 RAG Application | |
| F1-AI is a Retrieval-Augmented Generation (RAG) application specifically designed for Formula 1 information. It features an intelligent web scraper that automatically discovers and extracts Formula 1-related content from the web, stores it in a vector database, and enables natural language querying of the stored information. | |
| ## Features | |
|  | |
| - Web scraping of Formula 1 content with automatic content extraction | |
| - Vector database storage using Pinecone for efficient similarity search | |
| - OpenRouter integration with Mistral-7B-Instruct model for advanced LLM capabilities | |
| - HuggingFace embeddings for improved semantic understanding | |
| - RAG-powered question answering with contextual understanding and source citations | |
| - Command-line interface for automation and scripting | |
| - User-friendly Streamlit web interface with chat history | |
| - Asynchronous data ingestion and processing for improved performance | |
| ## Architecture | |
| F1-AI is built on a modern tech stack: | |
| - **LangChain**: Orchestrates the RAG pipeline and manages interactions between components | |
| - **Pinecone**: Vector database for storing and retrieving embeddings | |
| - **OpenRouter**: Primary LLM provider with Mistral-7B-Instruct model | |
| - **HuggingFace**: Provides all-MiniLM-L6-v2 embeddings model | |
| - **Playwright**: Handles web scraping with JavaScript support | |
| - **BeautifulSoup4**: Processes HTML content and extracts relevant information | |
| - **Streamlit**: Provides an interactive web interface with chat functionality | |
| ## Prerequisites | |
| - Python 3.8 or higher | |
| - OpenRouter API key (set as OPENROUTER_API_KEY environment variable) | |
| - Pinecone API key (set as PINECONE_API_KEY environment variable) | |
| - 8GB RAM minimum (16GB recommended) | |
| - Internet connection for web scraping | |
| ## Installation | |
| 1. Clone the repository: | |
| ```bash | |
| git clone <repository-url> | |
| cd f1-ai | |
| ``` | |
| 2. Install the required dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 3. Install Playwright browsers: | |
| ```bash | |
| playwright install chromium | |
| ``` | |
| 4. Set up environment variables: | |
| Create a .env file with: | |
| ``` | |
| OPENROUTER_API_KEY=your_api_key_here # Required for LLM functionality | |
| PINECONE_API_KEY=your_api_key_here # Required for vector storage | |
| ``` | |
| ## Usage | |
| ### Command Line Interface | |
| 1. Scrape and ingest F1 content: | |
| ```bash | |
| python f1_scraper.py --start-urls https://www.formula1.com/ --max-pages 100 --depth 2 --ingest | |
| ``` | |
| Options: | |
| - `--start-urls`: Space-separated list of URLs to start crawling from | |
| - `--max-pages`: Maximum number of pages to crawl (default: 100) | |
| - `--depth`: Maximum crawl depth (default: 2) | |
| - `--ingest`: Flag to ingest discovered content into RAG system | |
| - `--max-chunks`: Maximum chunks per URL for ingestion (default: 50) | |
| - `--llm-provider`: Choose LLM provider (openrouter) | |
| 2. Ask questions about Formula 1: | |
| ```bash | |
| python f1_ai.py ask "Who won the 2023 F1 World Championship?" | |
| ``` | |
| ### Streamlit Interface | |
| Run the Streamlit app: | |
| ```bash | |
| streamlit run app.py | |
| ``` | |
| This will open a web interface where you can: | |
| - Ask questions about Formula 1 | |
| - View responses in a chat-like interface | |
| - See source citations for answers | |
| - Track conversation history | |
| - Get real-time updates on response generation | |
| ## Project Structure | |
| - `f1_scraper.py`: Intelligent web crawler implementation | |
| - Automatically discovers F1-related content using keyword scoring | |
| - Handles content relevance detection with priority paths | |
| - Manages crawling depth and limits | |
| - Implements domain-specific filtering | |
| - `f1_ai.py`: Core RAG application implementation | |
| - Handles data ingestion and chunking | |
| - Manages vector database operations | |
| - Implements question-answering logic with source tracking | |
| - Provides robust error handling | |
| - `llm_manager.py`: LLM provider management | |
| - Integrates with OpenRouter for advanced LLM capabilities | |
| - Manages HuggingFace embeddings generation | |
| - Implements rate limiting and error recovery | |
| - Handles async API interactions | |
| - `app.py`: Streamlit web interface | |
| - Provides chat-based UI with message history | |
| - Manages conversation state | |
| - Handles async operations with progress tracking | |
| - Implements error handling and user feedback | |
| ## Contributing | |
| Contributions are welcome! Please follow these steps: | |
| 1. Fork the repository | |
| 2. Create a feature branch | |
| 3. Commit your changes | |
| 4. Push to the branch | |
| 5. Submit a Pull Request |