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-# Studyson - RAG Document QA & Summarization API
-
-A full-stack Retrieval-Augmented Generation (RAG) system for intelligent document question-answering and summarization. Built with FastAPI, LlamaIndex, and Groq AI.
-
-## Features
-
-- **📄 PDF Document Processing**: Upload and index PDF documents with intelligent text extraction
-- **🌐 Web Content Scraping**: Scrape and index content from URLs
-- **💬 Interactive Q&A Chat**: Ask questions about your documents with streaming responses
-- **📝 Smart Summarization**: Generate concise summaries of indexed documents
-- **🔍 Source Citations**: Get verifiable citations with exact source snippets
-- **⚡ Real-time Streaming**: Token-by-token streaming for responsive user experience
-- **🎨 Modern UI**: Clean, responsive web interface with tabbed navigation
-- **🐳 Docker Support**: Easy deployment with Docker and Docker Compose
-
-## Tech Stack
-
-### Backend
-- **FastAPI**: Modern Python web framework
-- **LlamaIndex**: RAG orchestration and document indexing
-- **Groq**: Lightning-fast LLM inference (Llama 3.1)
-- **FastEmbed**: Lightweight embeddings (BGE-small)
-- **PyMuPDF**: Advanced PDF text extraction
-- **BeautifulSoup**: HTML parsing and web scraping
-- **Pydantic**: Data validation and settings management
-
-### Frontend
-- **HTML5/CSS3/JavaScript**: Vanilla web technologies
-- **Server-Sent Events (SSE)**: Real-time streaming responses
-
-## Architecture
-
-### Ingestion Pipeline
-1. User uploads PDF or provides URL
-2. Content extraction (PyMuPDF for PDFs, BeautifulSoup for web)
-3. Text chunking and embedding via LlamaIndex + FastEmbed
-4. In-memory vector index creation
-
-### Query Pipeline
-1. Question embedding generation
-2. Semantic similarity search for relevant chunks
-3. Context + question sent to Groq LLM
-4. Streaming response with source citations
-
-## Installation
-
-### Prerequisites
-- Python 3.10 or higher
-- Groq API key ([Get it free here](https://console.groq.com))
-
-### Local Setup
-
-1. **Clone the repository**
-```bash
-git clone <repository-url>
-cd studyrag
-```
-
-2. **Create virtual environment**
-```bash
-python -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
-```
-
-3. **Install dependencies**
-```bash
-pip install -r requirements.txt
-```
-
-4. **Set up environment variables**
-```bash
-cp .env.example .env
-```
-
-Edit `.env` and add your Groq API key:
-```
-GROQ_API_KEY=your_groq_api_key_here
-PORT=7860
-HOST=0.0.0.0
-```
-
-5. **Run the application**
-```bash
-uvicorn app.main:app --reload --port 7860
-```
-
-6. **Access the application**
-
-Open your browser and navigate to: `http://localhost:7860`
-
-### Docker Setup
-
-1. **Set environment variables**
-```bash
-cp .env.example .env
-# Edit .env with your Groq API key
-```
-
-2. **Build and run with Docker Compose**
-```bash
-docker-compose up --build
-```
-
-## API Endpoints
-
-| Method | Endpoint | Description |
-|--------|----------|-------------|
-| GET | `/` | Serves the web UI |
-| POST | `/upload` | Upload PDF document |
-| POST | `/scrape` | Scrape URL content |
-| POST | `/stream_query` | Stream Q&A response |
-| POST | `/query` | Get Q&A response |
-| POST | `/summarize` | Generate summary |
-| POST | `/reset` | Clear all documents |
-| GET | `/status` | Get system status |
-
-## Project Structure
-
-```
-studyrag/
-├── app/
-│   ├── __init__.py
-│   ├── main.py              # FastAPI application
-│   ├── config.py            # Configuration settings
-│   ├── models/
-│   │   └── schemas.py       # Pydantic models
-│   ├── services/
-│   │   └── rag_service.py   # RAG logic
-│   └── utils/
-│       └── document_processor.py
-├── static/
-│   ├── css/style.css
-│   ├── js/app.js
-│   └── index.html
-├── .env.example
-├── .gitignore
-├── Dockerfile
-├── docker-compose.yml
-├── Procfile
-├── requirements.txt
-└── README.md
-```
-
-## Configuration
-
-### Environment Variables
-
-- `GROQ_API_KEY`: Your Groq API key (required, free tier available)
-- `HOST`: Server host (default: 0.0.0.0)
-- `PORT`: Server port (default: 7860)
-
-### Application Settings
-
-Edit `app/config.py` to modify:
-- `upload_dir`: Upload directory path
-- `max_file_size`: Maximum file size (default: 10MB)
-
-## Deployment
-
-### Deploy to Hugging Face Spaces (Recommended - Free)
-
-1. Push code to GitHub
-2. Go to [huggingface.co](https://huggingface.co) and create an account
-3. Click your profile → **New Space**
-4. Configure:
-   - **Space name**: `studyson`
-   - **SDK**: Select **Docker**
-   - **Hardware**: CPU basic (free)
-5. Under **Files** → Link to GitHub repo (or upload files)
-6. Add secret: `GROQ_API_KEY` in Space Settings → Variables
-7. The Space will auto-build and deploy!
-
-**Your app will be live at:** `https://huggingface.co/spaces/YOUR_USERNAME/studyson`
-
-## Features in Detail
-
-### RAG Pipeline
-- **Chunking**: Intelligent text splitting for optimal context windows
-- **Embeddings**: FastEmbed BGE-small for semantic understanding (lightweight)
-- **Retrieval**: Top-k similarity search with configurable parameters
-- **Generation**: Groq Llama 3.1 for fast, accurate responses
-
-### Streaming
-- Server-Sent Events (SSE) for real-time token delivery
-- Progressive rendering in the UI
-- Graceful error handling
-
-### Source Attribution
-- Exact text snippets from source documents
-- Similarity scores for transparency
-- Multiple source support per answer
-
-## Limitations
-
-- In-memory vector storage (resets on restart)
-- PDF-only document support (extensible to other formats)
-- Single-user session management
-- No authentication/authorization
-
-## Troubleshooting
-
-### Common Issues
-
-**Import errors:**
-```bash
-pip install --upgrade -r requirements.txt
-```
-
-**API key errors:**
-- Verify your `.env` file has the correct `GROQ_API_KEY`
-- Check API key validity at [console.groq.com](https://console.groq.com)
-
-**Port already in use:**
-```bash
-uvicorn app.main:app --port 8000
-```
-
-**File upload fails:**
-- Check file size is under 10MB
-
-## License
-
-MIT License - feel free to use this project for learning and development.
-
-## Acknowledgments
-
-- [LlamaIndex](https://www.llamaindex.ai/) for RAG orchestration
-- [Groq](https://groq.com/) for lightning-fast LLM inference
-- [FastEmbed](https://github.com/qdrant/fastembed) for lightweight embeddings
-- [FastAPI](https://fastapi.tiangolo.com/) for the web framework
-
----
-
-Built with ❤️ using RAG technology