# 🚀 Antigravity Notebook - Quick Start Guide

Get up and running with Antigravity Notebook in 5 minutes!

## Step 1: Prerequisites

Ensure you have:
- ✅ Python 3.9 or higher
- ✅ Docker & Docker Compose
- ✅ CUDA GPU (recommended, 16GB+ VRAM) or CPU (slower)
- ✅ ~20GB free disk space (for model + data)

## Step 2: Installation

### Clone & Install

```bash
# Clone the repository
git clone <your-repo-url>
cd antigravity-notebook

# Install Python dependencies
pip install -r requirements.txt
```

### Configure Environment

```bash
# Copy environment template
cp .env.example .env

# (Optional) Edit .env if you want custom settings
# Default settings work out of the box!
```

## Step 3: Start PostgreSQL

```bash
# Start PostgreSQL with Docker Compose
docker-compose up -d

# Verify it's running
docker ps
```

You should see a container named `antigravity_postgres` running.

## Step 4: Initialize Database

```bash
# Create database tables
python -m backend.database
```

You should see: `✅ Database initialized successfully!`

## Step 5: Start Backend API

```bash
# Start the FastAPI backend
python -m backend.main
```

Wait for:
```
✅ CLaRa model loaded!
✅ Antigravity Notebook is ready!
📍 API: http://0.0.0.0:8000
📚 Docs: http://0.0.0.0:8000/docs
```

**Note**: First startup takes ~5 minutes to download CLaRa-7B model (14GB).

## Step 6: Start Frontend UI

Open a **new terminal** and run:

```bash
# Start Streamlit UI
streamlit run frontend/app_notebook.py
```

Your browser should automatically open to `http://localhost:8501`

## Step 7: Create Your First Notebook

### In the Streamlit UI:

1. **Create a Notebook**
   - Click "Create New Notebook" in the sidebar
   - Name it "My First Notebook"
   - Add a description (optional)
   - Click "Create Notebook"

2. **Add a Source**
   - Choose one of:
     - **PDF**: Upload a PDF file
     - **URL**: Paste a webpage URL (e.g., Wikipedia article)
     - **Text**: Paste some text content

   Example URL to try: `https://en.wikipedia.org/wiki/Artificial_intelligence`

3. **Wait for Processing**
   - The source will be compressed into latent tensors
   - PDF (50 pages): ~30 seconds
   - URL: ~20 seconds
   - Text: ~10 seconds

4. **Ask a Question**
   - Type a query in the chat box
   - Example: "What are the main points discussed in this document?"
   - Press Enter
   - Wait ~10 seconds for response

5. **View Sources**
   - Click "Sources" under the response to see which sources were cited
   - Check the Memory Usage gauge to see context utilization

## 🎉 You're Done!

You now have a working NotebookLM clone!

## 📚 Next Steps

### Try These Examples:

#### Example 1: Research Assistant
1. Create a notebook called "AI Research"
2. Add these Wikipedia URLs:
   - https://en.wikipedia.org/wiki/Artificial_intelligence
   - https://en.wikipedia.org/wiki/Machine_learning
   - https://en.wikipedia.org/wiki/Deep_learning
3. Ask: "Compare and contrast AI, ML, and Deep Learning"

#### Example 2: Document Analysis
1. Create a notebook called "Company Docs"
2. Upload 3-5 PDF reports or documents
3. Ask: "Summarize the key findings across all documents"

#### Example 3: Web Research
1. Create a notebook called "Topic Research"
2. Add 5-10 URLs about a topic you're interested in
3. Ask questions that require synthesizing information across sources

## 🛠️ Common Issues

### Issue: "Database connection failed"
**Solution**: Ensure PostgreSQL is running
```bash
docker-compose up -d
docker ps  # Check if container is running
```

### Issue: "CUDA out of memory"
**Solution**: Use CPU mode
```bash
# Edit .env
DEVICE=cpu
```

### Issue: "Model download is slow"
**Solution**: Be patient! CLaRa-7B is 14GB. It only downloads once.
Check progress at: `./model_cache/`

### Issue: "PDF extraction failed"
**Solution**: Ensure PDF has extractable text (not scanned images)

### Issue: "URL scraping failed"
**Solution**: Some websites block scraping. Try a different URL.

## 🔧 Configuration Tips

### For CPU-Only Systems

Edit `.env`:
```env
DEVICE=cpu
```

### For Limited Memory

Reduce context window in `.env`:
```env
MAX_CONTEXT_TOKENS=16384  # Half the default
```

### For Production Use

1. **Change database password**:
   ```env
   POSTGRES_PASSWORD=<secure-password>
   ```

2. **Set secret key**:
   ```env
   SECRET_KEY=<random-secure-key>
   ```

3. **Configure CORS** in `backend/main.py`:
   ```python
   allow_origins=["https://your-frontend-domain.com"]
   ```

## 📊 Monitoring

### Check API Health
```bash
curl http://localhost:8000/health
```

### View Storage Stats
```bash
curl http://localhost:8000/stats
```

### API Documentation
Open: http://localhost:8000/docs

Try the interactive API explorer!

## 🧪 Testing the API Directly

### Create a Notebook
```bash
curl -X POST http://localhost:8000/notebooks/ \
  -H "Content-Type: application/json" \
  -d '{"name": "Test Notebook", "description": "API test"}'
```

### List Notebooks
```bash
curl http://localhost:8000/notebooks/
```

### Add Text Source
```bash
curl -X POST http://localhost:8000/sources/notebooks/<NOTEBOOK_ID>/sources/text \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Test Document",
    "content": "This is a test document with some content to analyze."
  }'
```

### Query Notebook
```bash
curl -X POST http://localhost:8000/chat/notebooks/<NOTEBOOK_ID>/chat \
  -H "Content-Type: application/json" \
  -d '{"query": "What is this document about?"}'
```

## 🎓 Learning More

- **Read the Plan**: See `ANTIGRAVITY_PLAN.md` for architecture details
- **Explore the Code**: Check out:
  - `backend/services/context_manager.py` - The "brain"
  - `backend/models/clara.py` - CLaRa wrapper
  - `backend/services/ingestion.py` - Multi-modal processing

## 💡 Pro Tips

1. **Start Small**: Begin with 1-2 sources to understand the system
2. **Check Memory Usage**: Watch the gauge to see when you hit limits
3. **Use Descriptive Titles**: Makes it easier to understand citations
4. **Mix Source Types**: PDFs + URLs + Text work great together
5. **Ask Synthesis Questions**: The AI excels at combining information

## 🆘 Need Help?

- **Documentation**: See `README.md`
- **API Docs**: http://localhost:8000/docs
- **Issues**: Open a GitHub issue
- **Logs**: Check terminal output for debugging

---

**Happy NotebookLM-ing! 🚀**