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AI Station Server
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README.md
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# AI Station - Document Analysis Platform
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# AI Station DFFM - Vision-RAG Hybrid System
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## 📋 Overview
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Sistema AI multi-utente con supporto RAG (Retrieval-Augmented Generation), analisi immagini e gestione documenti avanzata, basato su Chainlit, Ollama e BGE-M3.
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**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**.
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## 🌟 Features
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### Core AI
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- **RAG Hybrid Search** con BGE-M3 (dense + sparse embeddings)
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- **Vision Analysis** tramite MiniCPM-V per OCR e descrizione immagini
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- **Document Processing** con Docling (PDF, DOCX) con preservazione tabelle/formule
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- **Multi-Model Support** (Ollama locale + cloud models)
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- **Streaming Responses** con latenza ridotta
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### Multi-Utente
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- **OAuth2 Google** con profili personalizzati per ruolo
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- **Workspace isolati** per utente/team
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- **RAG Collections dedicate** per knowledge base separate
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- **Permessi granulari** (admin, engineering, business, architecture)
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### UI/UX
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- **Badge ruolo personalizzato** con colori dedicati
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- **Settings dinamici** (temperatura, top_k RAG, modello, istruzioni custom)
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- **Chat history persistente** con ripresa conversazioni
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- **Auto-save codice Python** estratto dalle risposte
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- **Metriche real-time** (response time, RAG hits, errori)
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### Performance
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- **Caching embeddings** (LRU cache 1000 query)
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- **Chunking intelligente** (2000 char con overlap 200)
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- **Async operations** su Qdrant e Ollama
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- **PostgreSQL** per persistenza thread e metadata
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## 🏗️ Architettura
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### Hardware Setup
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#### AI-SRV (Chainlit VM)
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- **IP**: 192.168.1.244
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- **CPU**: 16 core (QEMU Virtual)
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- **RAM**: 64 GB
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- **Storage**: 195 GB
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- **Ruolo**: Host Chainlit app + PostgreSQL + Qdrant
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#### AI-Server (GPU Workstation)
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- **IP**: 192.168.1.243
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- **CPU**: Intel Core Ultra 7 265 (20 core, max 6.5 GHz)
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- **RAM**: 32 GB
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- **GPU**: NVIDIA RTX A1000 (8 GB VRAM)
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- **Storage**: 936 GB NVMe
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- **Ruolo**: Ollama models + BGE-M3 embeddings service
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### Stack Tecnologico
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- **Backend**: Python + Chainlit (LLM UI framework)
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- **LLM**: GLM-4.6:Cloud (via Ollama Cloud)
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- **Vector DB**: Qdrant (semantic search)
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- **PDF Processing**: PyMuPDF (fitz)
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- **Database**: PostgreSQL + SQLAlchemy ORM
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- **Containerization**: Docker Compose
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- **Embeddings**: nomic-embed-text (via Ollama local)
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---
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┌─────────────────────────────────────────┐
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│ Chainlit UI (ai-srv) │
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│ Badge + Settings + Chat History │
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└──────────────┬──────────────────────────┘
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│
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┌──────────────▼──────────────────────────┐
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│ Python Backend (app.py) │
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│ - OAuth2 Google │
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│ - Multi-user profiles │
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│ - File processing orchestration │
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└─┬────────┬──────────┬──────────┬────────┘
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│ │ │ │
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▼ ▼ ▼ ▼
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┌─────┐ ┌─────┐ ┌────────┐ ┌──────────┐
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│ PG │ │Qdrant│ │ Ollama │ │ BGE API │
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│ │ │Vector│ │ GPU │ │ CPU │
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│ │ │ DB │ │ Server │ │ Server │
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└─────┘ └─────┘ └────────┘ └──────────┘
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ai-srv ai-srv ai-server ai-server
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## 🚀 Quick Start
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text
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### Prerequisites
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- Docker & Docker Compose
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- Ollama installed locally (for embeddings)
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- Ollama Cloud account (for glm-4.6:cloud)
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## 📋 Requisiti
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### 1️⃣ Clone & Setup
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### Sistema
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- Docker 24.x+ con Docker Compose
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- Accesso a Google Cloud Console (per OAuth2)
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- 2 server (o VM) con networking condiviso
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### Modelli Ollama (da installare su ai-server)
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```bash
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git clone git@github.com:your-username/ai-station.git
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ollama pull minicpm-v # Vision model (5.5 GB)
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ollama pull glm-4.6:cloud # Cloud reasoning
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ollama pull qwen2.5-coder:32b # Code generation (9 GB)
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ollama pull llama3.2 # Fast general purpose (4.7 GB)
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🚀 Installazione
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1. Clone Repository
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bash
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git clone <your-repo>
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cd ai-station
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2. Configurazione Ambiente
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Crea .env:
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# Configure environment
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cat > .env << 'EOF'
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DATABASE_URL=postgresql+asyncpg://ai_user:secure_password_here@postgres:5432/ai_station
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bash
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# Database
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DATABASE_URL=postgresql+asyncpg://ai_user:CHANGE_ME@postgres:5432/ai_station
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# AI Services
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OLLAMA_URL=http://192.168.1.243:11434
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QDRANT_URL=http://qdrant:6333
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EOF
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```
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BGE_API_URL=http://192.168.1.243:8001/embed
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### 2️⃣ Authenticate Ollama Cloud
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```bash
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ollama signin
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# Follow the link to authenticate with your Ollama account
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```
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# OAuth Google
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OAUTH_GOOGLE_CLIENT_ID=your-client-id.apps.googleusercontent.com
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OAUTH_GOOGLE_CLIENT_SECRET=your-secret
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CHAINLIT_AUTH_SECRET=$(openssl rand -base64 32)
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### 3️⃣ Start Services
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```bash
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docker compose up -d
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3. Configurazione OAuth Google
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Vai su Google Cloud Console
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Crea nuovo progetto → API e servizi → Credenziali
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Crea "ID client OAuth 2.0"
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Aggiungi URI autorizzati:
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https://ai.dffm.it/auth/oauth/google/callback
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http://localhost:8000/auth/oauth/google/callback (dev)
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Copia Client ID e Secret in .env
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4. Personalizza Utenti
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Modifica app.py → USER_PROFILES:
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python
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USER_PROFILES = {
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"tuo.email@example.com": {
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"role": "admin",
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"name": "Nome",
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"workspace": "workspace_name",
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"rag_collection": "docs_collection",
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"capabilities": ["debug", "all"],
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"show_code": True,
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},
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# ... altri utenti
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}
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5. Deploy
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bash
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# Build e avvio
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docker compose up -d --build
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# Verifica logs
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docker compose logs -f chainlit-app
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# Dovresti vedere:
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# ✅ Tutte le tabelle create con successo.
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# Your app is available at http://localhost:8000
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6. Setup BGE-M3 Service (su ai-server)
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bash
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# Installa dependencies
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pip install fastapi uvicorn FlagEmbedding torch
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# Salva il file bge_service.py (vedi docs/)
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python bge_service.py
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# Listening on http://0.0.0.0:8001
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🎯 Utilizzo
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Login
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Accedi via browser: https://ai.dffm.it (o http://localhost:8000)
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Click su "Continue with Google"
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Autorizza con account configurato in USER_PROFILES
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Chat con RAG
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Carica PDF/DOCX → Sistema li indicizza automaticamente
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Fai domande → Risposta con contesto dai documenti
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Regola top_k (numero documenti) via settings
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Analisi Immagini
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Carica screenshot/diagrammi
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Il sistema:
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Estrae testo (OCR)
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Descrive grafici/tabelle
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Usa descrizione come contesto per rispondere
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Settings Disponibili
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Numero Documenti RAG (1-10): Quanti chunk recuperare
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Modello: Scegli tra locale/cloud
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Temperatura (0-1): Creatività risposta
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RAG Enabled: On/Off recupero documenti
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Istruzione Custom: Prompt system personalizzato
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Ripresa Chat
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Sidebar → Chat History
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Click su conversazione → "Riprendi"
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Continua da dove avevi lasciato
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📊 Metriche
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Ogni risposta logga (stdout):
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json
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{
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"response_time": 18.65,
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"rag_hits": 4,
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"model": "glm-4.6:cloud",
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"user_role": "admin",
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"error": null
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}
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Raccogli con:
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bash
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docker logs ai-station-app | grep METRICS > metrics.log
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🔧 Troubleshooting
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RAG non trova documenti
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Verifica collection name in USER_PROFILES[email]["rag_collection"]
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Controlla Qdrant: curl http://localhost:6333/collections
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Badge HTML non si vede
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Abilita in .chainlit/config.toml:
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```
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### 4️⃣ Access UI
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Navigate to: **http://localhost:8000**
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---
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## 📁 Project Structure
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```text
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[features]
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unsafe_allow_html = true
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Modello Ollama non risponde
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bash
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# Testa connessione
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curl http://192.168.1.243:11434/api/tags
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# Verifica modello disponibile
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ollama list
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BGE embeddings fail
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```
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```bash
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# Testa API
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curl -X POST http://192.168.1.243:8001/embed \
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-H "Content-Type: application/json" \
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-d '{"texts": ["test"]}'
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```
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📁 Struttura Progetto
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```bash
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ai-station/
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├── app.py # Main Chainlit application
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├── requirements.txt # Python dependencies
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├── docker-compose.yml # Docker services config
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├── .env # Environment variables (gitignored)
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├── workspaces/ # User workspace directories
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│ └── admin/ # Admin user files
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└── README.md # This file
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├── app.py # Main Chainlit app
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├── init_db.py # Database schema init
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├── requirements.txt # Python deps
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├── Dockerfile # Container config
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├── docker-compose.yaml # Multi-service orchestration
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├── .chainlit/
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│ └── config.toml # UI/features config
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├── public/
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│ └── custom.css # Custom styling
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├── workspaces/ # User file storage (volume)
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│ ├── admin_workspace/
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│ ├── engineering_workspace/
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│ └── ...
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└── .files/ # Chainlit storage (volume)
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```
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---
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## 🔧 Features
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### ✅ Implemented
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- **PDF Upload & Processing**: Extract text from PDF documents using PyMuPDF
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- **Document Indexing**: Automatic chunking and semantic indexing via Qdrant
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- **RAG Search**: Retrieve relevant document chunks based on semantic similarity
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- **Intelligent Analysis**: GLM-4.6:Cloud analyzes documents with full context
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- **Code Extraction**: Automatically save Python code blocks from responses
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- **Chat History**: Persistent conversation storage via SQLAlchemy
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- **Streaming Responses**: Real-time token streaming via Chainlit
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### 🔄 Workflow
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1. User uploads PDF or TXT file
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2. System extracts text and creates semantic chunks
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3. Chunks indexed in Qdrant vector database
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4. User asks questions about documents
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5. RAG retrieves relevant chunks
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6. GLM-4.6:Cloud analyzes with full context
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7. Streaming response to user
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---
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## 📊 Technical Details
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### Document Processing Pipeline
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```
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PDF Upload
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↓
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PyMuPDF Text Extraction
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↓
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Text Chunking (1500 chars, 200 char overlap)
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↓
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nomic-embed-text Embeddings (Ollama local)
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↓
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Qdrant Vector Storage
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↓
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Semantic Search on User Query
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↓
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GLM-4.6:Cloud Analysis with RAG Context
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↓
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Chainlit Streaming Response
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```
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### Key Functions
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| Function | Purpose |
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|----------|---------|
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| `extract_text_from_pdf()` | Convert PDF to text using PyMuPDF |
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| `chunk_text()` | Split text into overlapping chunks |
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| `get_embeddings()` | Generate embeddings via Ollama |
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| `index_document()` | Store chunks in Qdrant |
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| `search_qdrant()` | Retrieve relevant context |
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| `on_message()` | Process user queries with RAG |
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---
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## 🔐 Environment Variables
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```env
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DATABASE_URL=postgresql+asyncpg://user:pass@postgres:5432/ai_station
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OLLAMA_URL=http://192.168.1.243:11434 # Local Ollama for embeddings
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QDRANT_URL=http://qdrant:6333 # Vector database
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```
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**Note**: GLM-4.6:Cloud authentication is handled automatically via `ollama signin`
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---
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## 🐳 Docker Services
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| Service | Port | Purpose |
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|---------|------|---------|
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| `chainlit-app` | 8000 | Chainlit UI & API |
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| `postgres` | 5432 | Conversation persistence |
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| `qdrant` | 6333 | Vector database |
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| `ollama` | 11434 | Local embeddings (external) |
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Start/Stop:
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🔐 Sicurezza
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```bash
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docker compose up -d # Start all services
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docker compose down # Stop all services
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docker compose logs -f # View logs
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docker compose restart # Restart services
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OAuth2 obbligatorio (no accesso anonimo)
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Workspace isolation (file separati per utente)
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HTML sanitization (configurable via unsafe_allow_html)
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Environment secrets (.env mai committato)
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PostgreSQL passwords cambiate da default
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```
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---
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## 📝 Usage Examples
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### Example 1: Analyze Tax Document
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```
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User: "Qual è l'importo totale del documento?"
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AI Station:
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✅ Extracts PDF content
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✅ Searches relevant sections
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✅ Analyzes with GLM-4.6:Cloud
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📄 Returns: "Based on the document, the total amount is..."
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```
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### Example 2: Multi-Document Analysis
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```
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1. Upload multiple PDFs (invoices, contracts)
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2. All documents automatically indexed
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3. Query across all documents simultaneously
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4. RAG retrieves most relevant chunks
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5. GLM-4.6:Cloud synthesizes answer
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```
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---
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## 🛠️ Development
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### Install Dependencies
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🚦 Roadmap
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```bash
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pip install -r requirements.txt
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Re-ranking con cross-encoder
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Query expansion automatica
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Feedback loop (👍👎 su risposte)
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Export conversazioni PDF/Markdown
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Multi-query RAG parallelo
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Prometheus/Grafana monitoring
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Adaptive chunking per tipo documento
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Audio input support
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```
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### Requirements
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```
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chainlit==1.3.2
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pydantic==2.9.2
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ollama>=0.1.0
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asyncpg>=0.29.0
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psycopg2-binary
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qdrant-client>=1.10.0
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sqlalchemy>=2.0.0
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greenlet>=3.0.0
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sniffio
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aiohttp
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alembic
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pymupdf
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python-dotenv
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## 📝 Licenza
|
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```tect
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MIT License - vedi file [LICENSE](LICENSE) per dettagli.
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Crea file LICENSE nella root del progetto
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text
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MIT License
|
||||
|
||||
Copyright (c) 2026 DFFM / Giuseppe De Franceschi
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||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
```
|
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|
||||
### Local Testing (without Docker)
|
||||
```bash
|
||||
# Start Ollama, PostgreSQL, Qdrant manually
|
||||
ollama serve &
|
||||
chainlit run app.py
|
||||
```
|
||||
👥 Contributors
|
||||
Giuseppe De Franceschi - @defranceschi
|
||||
|
||||
---
|
||||
🙏 Credits
|
||||
Chainlit - UI framework
|
||||
|
||||
## 🔄 Model Details
|
||||
Ollama - LLM runtime
|
||||
|
||||
### 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`)
|
||||
Qdrant - Vector DB
|
||||
|
||||
### nomic-embed-text
|
||||
- **Local embedding model** for chunking/retrieval
|
||||
- **Dimensions**: 768
|
||||
- **Speed**: Fast, runs locally
|
||||
- **Used for**: RAG semantic search
|
||||
BGE-M3 - Embeddings
|
||||
|
||||
---
|
||||
Docling - Document processing
|
||||
|
||||
## 📈 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 ✅
|
||||
## **Status**: 🔨 Pre-Production | **Last Update**: 2026-01-01
|
||||
Loading…
Reference in New Issue