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

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

ollama signin
# Follow the link to authenticate with your Ollama account

3️⃣ Start Services

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

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:

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

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)

# 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

# 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 ✅