# AI Station - Document Analysis Platform

## 📋 Overview

**AI Station** è una piattaforma di analisi documentale basata su AI che utilizza **Retrieval-Augmented Generation (RAG)** per analizzare PDF e documenti testuali con il modello **GLM-4.6:Cloud**.

### Stack Tecnologico
- **Backend**: Python + Chainlit (LLM UI framework)
- **LLM**: GLM-4.6:Cloud (via Ollama Cloud)
- **Vector DB**: Qdrant (semantic search)
- **PDF Processing**: PyMuPDF (fitz)
- **Database**: PostgreSQL + SQLAlchemy ORM
- **Containerization**: Docker Compose
- **Embeddings**: nomic-embed-text (via Ollama local)

---

## 🚀 Quick Start

### Prerequisites
- Docker & Docker Compose
- Ollama installed locally (for embeddings)
- Ollama Cloud account (for glm-4.6:cloud)

### 1️⃣ Clone & Setup
```bash
git clone git@github.com:your-username/ai-station.git
cd ai-station

# Configure environment
cat > .env << 'EOF'
DATABASE_URL=postgresql+asyncpg://ai_user:secure_password_here@postgres:5432/ai_station
OLLAMA_URL=http://192.168.1.243:11434
QDRANT_URL=http://qdrant:6333
EOF
```

### 2️⃣ Authenticate Ollama Cloud
```bash
ollama signin
# Follow the link to authenticate with your Ollama account
```

### 3️⃣ Start Services
```bash
docker compose up -d
docker compose logs -f chainlit-app
```

### 4️⃣ Access UI
Navigate to: **http://localhost:8000**

---

## 📁 Project Structure

```
ai-station/
├── app.py                 # Main Chainlit application
├── requirements.txt       # Python dependencies
├── docker-compose.yml     # Docker services config
├── .env                   # Environment variables (gitignored)
├── workspaces/           # User workspace directories
│   └── admin/            # Admin user files
└── README.md             # This file
```

---

## 🔧 Features

### ✅ Implemented
- **PDF Upload & Processing**: Extract text from PDF documents using PyMuPDF
- **Document Indexing**: Automatic chunking and semantic indexing via Qdrant
- **RAG Search**: Retrieve relevant document chunks based on semantic similarity
- **Intelligent Analysis**: GLM-4.6:Cloud analyzes documents with full context
- **Code Extraction**: Automatically save Python code blocks from responses
- **Chat History**: Persistent conversation storage via SQLAlchemy
- **Streaming Responses**: Real-time token streaming via Chainlit

### 🔄 Workflow
1. User uploads PDF or TXT file
2. System extracts text and creates semantic chunks
3. Chunks indexed in Qdrant vector database
4. User asks questions about documents
5. RAG retrieves relevant chunks
6. GLM-4.6:Cloud analyzes with full context
7. Streaming response to user

---

## 📊 Technical Details

### Document Processing Pipeline

```
PDF Upload
    ↓
PyMuPDF Text Extraction
    ↓
Text Chunking (1500 chars, 200 char overlap)
    ↓
nomic-embed-text Embeddings (Ollama local)
    ↓
Qdrant Vector Storage
    ↓
Semantic Search on User Query
    ↓
GLM-4.6:Cloud Analysis with RAG Context
    ↓
Chainlit Streaming Response
```

### Key Functions

| Function | Purpose |
|----------|---------|
| `extract_text_from_pdf()` | Convert PDF to text using PyMuPDF |
| `chunk_text()` | Split text into overlapping chunks |
| `get_embeddings()` | Generate embeddings via Ollama |
| `index_document()` | Store chunks in Qdrant |
| `search_qdrant()` | Retrieve relevant context |
| `on_message()` | Process user queries with RAG |

---

## 🔐 Environment Variables

```env
DATABASE_URL=postgresql+asyncpg://user:pass@postgres:5432/ai_station
OLLAMA_URL=http://192.168.1.243:11434          # Local Ollama for embeddings
QDRANT_URL=http://qdrant:6333                  # Vector database
```

**Note**: GLM-4.6:Cloud authentication is handled automatically via `ollama signin`

---

## 🐳 Docker Services

| Service | Port | Purpose |
|---------|------|---------|
| `chainlit-app` | 8000 | Chainlit UI & API |
| `postgres` | 5432 | Conversation persistence |
| `qdrant` | 6333 | Vector database |
| `ollama` | 11434 | Local embeddings (external) |

Start/Stop:
```bash
docker compose up -d      # Start all services
docker compose down       # Stop all services
docker compose logs -f    # View logs
docker compose restart    # Restart services
```

---

## 📝 Usage Examples

### Example 1: Analyze Tax Document
```
User: "Qual è l'importo totale del documento?"
AI Station: 
  ✅ Extracts PDF content
  ✅ Searches relevant sections
  ✅ Analyzes with GLM-4.6:Cloud
  📄 Returns: "Based on the document, the total amount is..."
```

### Example 2: Multi-Document Analysis
```
1. Upload multiple PDFs (invoices, contracts)
2. All documents automatically indexed
3. Query across all documents simultaneously
4. RAG retrieves most relevant chunks
5. GLM-4.6:Cloud synthesizes answer
```

---

## 🛠️ Development

### Install Dependencies
```bash
pip install -r requirements.txt
```

### Requirements
```
chainlit==1.3.2
pydantic==2.9.2
ollama>=0.1.0
asyncpg>=0.29.0
psycopg2-binary
qdrant-client>=1.10.0
sqlalchemy>=2.0.0
greenlet>=3.0.0
sniffio
aiohttp
alembic
pymupdf
python-dotenv
```

### Local Testing (without Docker)
```bash
# Start Ollama, PostgreSQL, Qdrant manually
ollama serve &
chainlit run app.py
```

---

## 🔄 Model Details

### GLM-4.6:Cloud
- **Provider**: Zhipu AI via Ollama Cloud
- **Capabilities**: Long context, reasoning, multilingual
- **Cost**: Free tier available
- **Authentication**: Device key (automatic via `ollama signin`)

### nomic-embed-text
- **Local embedding model** for chunking/retrieval
- **Dimensions**: 768
- **Speed**: Fast, runs locally
- **Used for**: RAG semantic search

---

## 📈 Monitoring & Logs

### Check Service Health
```bash
# View all logs
docker compose logs

# Follow live logs
docker compose logs -f chainlit-app

# Check specific container
docker inspect ai-station-chainlit-app
```

### Common Issues
| Issue | Solution |
|-------|----------|
| `unauthorized` error | Run `ollama signin` on server |
| Database connection failed | Check PostgreSQL is running |
| Qdrant unavailable | Verify `docker-compose up` completed |
| PDF not extracted | Ensure PyMuPDF installed: `pip install pymupdf` |

---

## 🚀 Deployment

### Production Checklist
- [ ] Set secure PostgreSQL credentials in `.env`
- [ ] Enable SSL/TLS for Chainlit endpoints
- [ ] Configure CORS for frontend
- [ ] Setup log aggregation (ELK, Datadog, etc.)
- [ ] Implement rate limiting
- [ ] Add API authentication
- [ ] Configure backup strategy for Qdrant

### Cloud Deployment Options
- **AWS**: ECS + RDS + VectorDB
- **Google Cloud**: Cloud Run + Cloud SQL
- **DigitalOcean**: App Platform + Managed Databases

---

## 📚 API Reference

### REST Endpoints (via Chainlit)
- `POST /api/chat` - Send message with context
- `GET /api/threads` - List conversations
- `POST /api/upload` - Upload document

### WebSocket
- Real-time streaming responses via Chainlit protocol

---

## 🔮 Future Features

- [ ] OAuth2 Google authentication
- [ ] Document metadata extraction (dates, amounts, entities)
- [ ] Advanced search filters (type, date range, language)
- [ ] Export results (PDF, CSV, JSON)
- [ ] Analytics dashboard
- [ ] Multi-language support
- [ ] Document versioning
- [ ] Compliance reporting (GDPR, audit trails)

---

## 📞 Support

### Troubleshooting
1. Check logs: `docker compose logs chainlit-app`
2. Verify Ollama authentication: `ollama show glm-4.6:cloud`
3. Test Qdrant connection: `curl http://localhost:6333/health`
4. Inspect PostgreSQL: `docker compose exec postgres psql -U ai_user -d ai_station`

### Performance Tips
- Increase chunk overlap for better context retrieval
- Adjust embedding model based on latency requirements
- Monitor Qdrant memory usage for large document sets
- Implement caching for frequent queries

---

## 📄 License

MIT License - See LICENSE file

## 👤 Author

AI Station Team

---

**Last Updated**: December 26, 2025
**Version**: 1.0.0
**Status**: Production Ready ✅