feat(docker): add Docker support for IntelliDocs ML/OCR features

- Add OpenCV system dependencies to Dockerfile (libglib2.0-0, libsm6, libxext6, etc.)
- Update docker-compose.env with ML/OCR configuration variables
- Create docker-compose.intellidocs.yml optimized for ML/OCR features
- Add comprehensive DOCKER_SETUP_INTELLIDOCS.md guide
- Add test-intellidocs-features.sh script for verification
- Add docker/README_INTELLIDOCS.md documentation
- Update main README with IntelliDocs quick start section

New features now available in Docker:
- Phase 1: Performance optimizations (147x faster)
- Phase 2: Security hardening (A+ score)
- Phase 3: AI/ML features (BERT, NER, semantic search)
- Phase 4: Advanced OCR (tables, handwriting, forms)

Co-authored-by: dawnsystem <42047891+dawnsystem@users.noreply.github.com>
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# 🐳 Docker Setup Guide for IntelliDocs
Este documento proporciona instrucciones completas para ejecutar IntelliDocs con todas las nuevas funciones (IA/ML, OCR Avanzado, Seguridad, Rendimiento) usando Docker.
## 📋 Tabla de Contenidos
- [Requisitos Previos](#requisitos-previos)
- [Inicio Rápido](#inicio-rápido)
- [Configuración Detallada](#configuración-detallada)
- [Nuevas Funciones Disponibles](#nuevas-funciones-disponibles)
- [Construcción de la Imagen](#construcción-de-la-imagen)
- [Verificación de Funciones](#verificación-de-funciones)
- [Troubleshooting](#troubleshooting)
---
## 🔧 Requisitos Previos
### Hardware Recomendado
Para las nuevas funciones de IA/ML:
- **CPU**: 4+ cores (8+ recomendado)
- **RAM**: 8 GB mínimo (16 GB recomendado para ML/OCR avanzado)
- **Disco**: 20 GB mínimo (para modelos ML y datos)
- **GPU** (opcional): NVIDIA GPU con CUDA para aceleración ML
### Software
- Docker Engine 20.10+
- Docker Compose 2.0+
- (Opcional) NVIDIA Docker para soporte GPU
### Verificar Instalación
```bash
docker --version
docker compose version
```
---
## 🚀 Inicio Rápido
### Opción 1: Usando el Script de Instalación
```bash
bash -c "$(curl -L https://raw.githubusercontent.com/dawnsystem/IntelliDocs-ngx/main/install-paperless-ngx.sh)"
```
### Opción 2: Setup Manual
1. **Clonar el repositorio:**
```bash
git clone https://github.com/dawnsystem/IntelliDocs-ngx.git
cd IntelliDocs-ngx
```
2. **Configurar variables de entorno:**
```bash
cd docker/compose
cp docker-compose.env docker-compose.env.local
nano docker-compose.env.local
```
3. **Configurar valores mínimos requeridos:**
```bash
# Editar docker-compose.env.local
PAPERLESS_SECRET_KEY=$(openssl rand -base64 32)
PAPERLESS_TIME_ZONE=Europe/Madrid
PAPERLESS_OCR_LANGUAGE=spa
```
4. **Iniciar los contenedores:**
```bash
# Con SQLite (más simple)
docker compose -f docker-compose.sqlite.yml up -d
# O con PostgreSQL (recomendado para producción)
docker compose -f docker-compose.postgres.yml up -d
```
5. **Acceder a la aplicación:**
```
http://localhost:8000
```
6. **Crear superusuario:**
```bash
docker compose exec webserver python manage.py createsuperuser
```
---
## ⚙️ Configuración Detallada
### Variables de Entorno - Funciones Básicas
```bash
# Configuración básica
PAPERLESS_URL=https://intellidocs.example.com
PAPERLESS_SECRET_KEY=your-very-long-random-secret-key-here
PAPERLESS_TIME_ZONE=America/Los_Angeles
PAPERLESS_OCR_LANGUAGE=eng
# Usuario/Grupo para permisos de archivos
USERMAP_UID=1000
USERMAP_GID=1000
```
### Variables de Entorno - Nuevas Funciones ML/OCR
```bash
# Habilitar funciones avanzadas de IA/ML
PAPERLESS_ENABLE_ML_FEATURES=1
# Habilitar funciones avanzadas de OCR
PAPERLESS_ENABLE_ADVANCED_OCR=1
# Modelo de clasificación ML
# Opciones: distilbert-base-uncased (rápido), bert-base-uncased (más preciso)
PAPERLESS_ML_CLASSIFIER_MODEL=distilbert-base-uncased
# Aceleración GPU (requiere NVIDIA Docker)
PAPERLESS_USE_GPU=0
# Umbral de confianza para detección de tablas (0.0-1.0)
PAPERLESS_TABLE_DETECTION_THRESHOLD=0.7
# Habilitar reconocimiento de escritura a mano
PAPERLESS_ENABLE_HANDWRITING_OCR=1
# Directorio de caché para modelos ML
PAPERLESS_ML_MODEL_CACHE=/usr/src/paperless/.cache/huggingface
```
### Volúmenes Persistentes
```yaml
volumes:
- ./data:/usr/src/paperless/data # Base de datos SQLite y datos de app
- ./media:/usr/src/paperless/media # Documentos procesados
- ./consume:/usr/src/paperless/consume # Documentos a procesar
- ./export:/usr/src/paperless/export # Exportaciones
- ./ml_cache:/usr/src/paperless/.cache # Caché de modelos ML (NUEVO)
```
**IMPORTANTE**: Crear el directorio `ml_cache` para persistir los modelos ML descargados:
```bash
mkdir -p ./ml_cache
chmod 777 ./ml_cache
```
---
## 🎯 Nuevas Funciones Disponibles
### Fase 1: Optimización de Rendimiento ⚡
**Mejoras Implementadas:**
- 6 índices compuestos en base de datos
- Sistema de caché mejorado con Redis
- Invalidación automática de caché
**Resultado**: 147x mejora de rendimiento (54.3s → 0.37s)
**Uso**: Automático, no requiere configuración adicional.
---
### Fase 2: Refuerzo de Seguridad 🔒
**Mejoras Implementadas:**
- Rate limiting por IP
- 7 security headers (CSP, HSTS, X-Frame-Options, etc.)
- Validación multi-capa de archivos
**Resultado**: Security score mejorado de C a A+
**Configuración Recomendada:**
```bash
# En docker-compose.env.local
PAPERLESS_ENABLE_HTTP_REMOTE_USER=false
PAPERLESS_COOKIE_PREFIX=intellidocs
```
---
### Fase 3: Mejoras de IA/ML 🤖
**Funciones Disponibles:**
1. **Clasificación Automática con BERT**
- Precisión: 90-95% (vs 70-80% tradicional)
- Clasifica documentos automáticamente por tipo
2. **Named Entity Recognition (NER)**
- Extrae nombres, fechas, montos, emails automáticamente
- 100% automatización de entrada de datos
3. **Búsqueda Semántica**
- Encuentra documentos por significado, no solo palabras clave
- Relevancia mejorada en 85%
**Uso:**
```bash
# Habilitar todas las funciones ML
PAPERLESS_ENABLE_ML_FEATURES=1
# Usar modelo más preciso (requiere más RAM)
PAPERLESS_ML_CLASSIFIER_MODEL=bert-base-uncased
```
**Primer Uso**: Los modelos ML se descargan automáticamente en el primer inicio (~500MB-1GB). Esto puede tomar varios minutos.
---
### Fase 4: OCR Avanzado 📄
**Funciones Disponibles:**
1. **Extracción de Tablas**
- Precisión: 90-95%
- Detecta y extrae tablas automáticamente
- Exporta a CSV/Excel
2. **Reconocimiento de Escritura a Mano**
- Precisión: 85-92%
- Soporta múltiples idiomas
- Usa modelo TrOCR de Microsoft
3. **Detección de Formularios**
- Precisión: 95-98%
- Identifica campos de formularios
- Extrae datos estructurados
**Configuración:**
```bash
# Habilitar OCR avanzado
PAPERLESS_ENABLE_ADVANCED_OCR=1
# Ajustar sensibilidad de detección de tablas
PAPERLESS_TABLE_DETECTION_THRESHOLD=0.7 # Valores: 0.5 (más sensible) - 0.9 (más estricto)
# Habilitar reconocimiento de manuscritos
PAPERLESS_ENABLE_HANDWRITING_OCR=1
```
---
## 🏗️ Construcción de la Imagen
### Construir Imagen Local
Si necesitas modificar el código o construir una imagen personalizada:
```bash
# Desde la raíz del proyecto
docker build -t intellidocs-ngx:latest .
```
### Construir con Soporte GPU (Opcional)
Para usar aceleración GPU con NVIDIA:
1. **Instalar NVIDIA Container Toolkit:**
```bash
# Ubuntu/Debian
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker
```
2. **Modificar docker-compose:**
```yaml
services:
webserver:
# ... otras configuraciones
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
environment:
- PAPERLESS_USE_GPU=1
```
### Construir para Multi-Arquitectura
```bash
# Construir para AMD64 y ARM64
docker buildx build --platform linux/amd64,linux/arm64 -t intellidocs-ngx:latest .
```
---
## ✅ Verificación de Funciones
### 1. Verificar Contenedores en Ejecución
```bash
docker compose ps
```
Deberías ver:
- `webserver` (IntelliDocs)
- `broker` (Redis)
- `db` (PostgreSQL/MariaDB, si aplica)
### 2. Verificar Logs
```bash
# Ver logs generales
docker compose logs -f
# Ver logs solo del webserver
docker compose logs -f webserver
# Buscar errores
docker compose logs webserver | grep -i error
```
### 3. Verificar Dependencias ML/OCR
Ejecutar script de verificación dentro del contenedor:
```bash
# Crear script de test
docker compose exec webserver bash -c 'cat > /tmp/test_ml.py << EOF
import sys
print("Testing ML/OCR dependencies...")
try:
import torch
print(f"✓ torch {torch.__version__}")
except ImportError as e:
print(f"✗ torch: {e}")
try:
import transformers
print(f"✓ transformers {transformers.__version__}")
except ImportError as e:
print(f"✗ transformers: {e}")
try:
import cv2
print(f"✓ opencv {cv2.__version__}")
except ImportError as e:
print(f"✗ opencv: {e}")
try:
import sentence_transformers
print(f"✓ sentence-transformers {sentence_transformers.__version__}")
except ImportError as e:
print(f"✗ sentence-transformers: {e}")
print("\nAll checks completed!")
EOF
'
# Ejecutar test
docker compose exec webserver python /tmp/test_ml.py
```
### 4. Probar Funciones ML/OCR
Una vez que la aplicación esté corriendo:
1. **Subir un documento de prueba:**
- Navega a http://localhost:8000
- Sube un documento PDF o imagen
- Observa el proceso de OCR en los logs
2. **Verificar clasificación automática:**
- Después de procesar, verifica si el documento fue clasificado
- Ve a "Documents" → "Tags" para ver tags aplicados
3. **Probar búsqueda semántica:**
- Busca por conceptos en lugar de palabras exactas
- Ejemplo: busca "factura de electricidad" aunque el documento diga "recibo de luz"
4. **Verificar extracción de tablas:**
- Sube un documento con tablas
- Verifica que las tablas fueron detectadas y extraídas en los metadatos
---
## 🔧 Troubleshooting
### Problema: Contenedor no inicia / Error de dependencias
**Síntoma**: El contenedor se reinicia constantemente o muestra errores de import.
**Solución**:
```bash
# Reconstruir la imagen sin caché
docker compose build --no-cache
# Reiniciar contenedores
docker compose down
docker compose up -d
# Verificar logs
docker compose logs -f webserver
```
### Problema: Out of Memory al procesar documentos
**Síntoma**: El contenedor se detiene o está muy lento con documentos grandes.
**Solución**:
```bash
# Aumentar memoria asignada a Docker
# En Docker Desktop: Settings → Resources → Memory → 8GB+
# O limitar procesos simultáneos en docker-compose.env.local:
PAPERLESS_TASK_WORKERS=1
PAPERLESS_THREADS_PER_WORKER=1
```
### Problema: Modelos ML no se descargan
**Síntoma**: Errores sobre modelos no encontrados.
**Solución**:
```bash
# Verificar conectividad a Hugging Face
docker compose exec webserver ping -c 3 huggingface.co
# Descargar modelos manualmente
docker compose exec webserver python -c "
from transformers import AutoTokenizer, AutoModel
model_name = 'distilbert-base-uncased'
print(f'Downloading {model_name}...')
AutoTokenizer.from_pretrained(model_name)
AutoModel.from_pretrained(model_name)
print('Done!')
"
# Verificar caché de modelos
docker compose exec webserver ls -lah /usr/src/paperless/.cache/huggingface/
```
### Problema: GPU no es detectada
**Síntoma**: PAPERLESS_USE_GPU=1 pero usa CPU.
**Solución**:
```bash
# Verificar NVIDIA Docker
docker run --rm --gpus all nvidia/cuda:11.8.0-base-ubuntu22.04 nvidia-smi
# Verificar dentro del contenedor
docker compose exec webserver python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"
```
### Problema: OCR no funciona correctamente
**Síntoma**: Los documentos no son procesados o el texto no es extraído.
**Solución**:
```bash
# Verificar Tesseract
docker compose exec webserver tesseract --version
# Verificar idiomas instalados
docker compose exec webserver tesseract --list-langs
# Instalar idioma adicional si es necesario
docker compose exec webserver apt-get update && apt-get install -y tesseract-ocr-spa
```
### Problema: Permisos de archivos
**Síntoma**: Error al escribir en volúmenes.
**Solución**:
```bash
# Ajustar permisos de directorios locales
sudo chown -R 1000:1000 ./data ./media ./consume ./export ./ml_cache
# O configurar UID/GID en docker-compose.env.local:
USERMAP_UID=$(id -u)
USERMAP_GID=$(id -g)
```
---
## 📊 Monitoreo de Recursos
### Verificar Uso de Recursos
```bash
# Ver uso de CPU/memoria de contenedores
docker stats
# Ver solo IntelliDocs
docker stats $(docker compose ps -q webserver)
```
### Monitoreo de Modelos ML
```bash
# Ver tamaño de caché de modelos
du -sh ./ml_cache/
# Ver modelos descargados
docker compose exec webserver ls -lh /usr/src/paperless/.cache/huggingface/hub/
```
---
## 🎓 Mejores Prácticas
### Producción
1. **Usar PostgreSQL en lugar de SQLite**
```bash
docker compose -f docker-compose.postgres.yml up -d
```
2. **Configurar backups automáticos**
```bash
# Backup de base de datos
docker compose exec db pg_dump -U paperless paperless > backup.sql
# Backup de media
tar -czf media_backup.tar.gz ./media
```
3. **Usar HTTPS con reverse proxy**
- Nginx o Traefik frente a IntelliDocs
- Certificado SSL (Let's Encrypt)
4. **Monitorear logs y métricas**
- Integrar con Prometheus/Grafana
- Alertas para errores críticos
### Desarrollo
1. **Usar volumen para código fuente**
```yaml
volumes:
- ./src:/usr/src/paperless/src
```
2. **Modo debug**
```bash
PAPERLESS_DEBUG=true
PAPERLESS_LOGGING_LEVEL=DEBUG
```
---
## 📚 Recursos Adicionales
- **Documentación IntelliDocs**: Ver archivos en `/docs`
- **Bitácora Maestra**: `BITACORA_MAESTRA.md`
- **Guías de Implementación**:
- `FASE1_RESUMEN.md` - Performance
- `FASE2_RESUMEN.md` - Security
- `FASE3_RESUMEN.md` - AI/ML
- `FASE4_RESUMEN.md` - Advanced OCR
---
## 🤝 Soporte
Si encuentras problemas:
1. Revisa esta guía de troubleshooting
2. Consulta los logs: `docker compose logs -f`
3. Revisa `BITACORA_MAESTRA.md` para detalles de implementación
4. Abre un issue en GitHub con detalles del problema
---
**IntelliDocs** - Sistema de Gestión Documental con IA
Versión: 1.0.0 (basado en Paperless-ngx 2.19.5)
Última actualización: 2025-11-09

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@ -161,7 +161,14 @@ ARG RUNTIME_PACKAGES="\
zlib1g \
# Barcode splitter
libzbar0 \
poppler-utils"
poppler-utils \
# OpenCV system dependencies for ML/OCR features
libglib2.0-0 \
libsm6 \
libxext6 \
libxrender1 \
libgomp1 \
libgl1"
# Install basic runtime packages.
# These change very infrequently

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@ -55,6 +55,34 @@ A full list of [features](https://docs.paperless-ngx.com/#features) and [screens
# Getting started
## 🚀 IntelliDocs Quick Start (with ML/OCR Features)
**NEW**: IntelliDocs includes advanced AI/ML and OCR features. See [DOCKER_SETUP_INTELLIDOCS.md](DOCKER_SETUP_INTELLIDOCS.md) for the complete guide.
```bash
# Quick start with all new features
cd docker/compose
docker compose -f docker-compose.intellidocs.yml up -d
# Test the new features
cd ..
./test-intellidocs-features.sh
```
**What's New in IntelliDocs:**
- ⚡ **147x faster** performance with optimized caching
- 🔒 **A+ security score** with rate limiting and security headers
- 🤖 **BERT classification** with 90-95% accuracy
- 📊 **Table extraction** from documents (90-95% accuracy)
- ✍️ **Handwriting recognition** (85-92% accuracy)
- 🔍 **Semantic search** for better document discovery
For detailed Docker setup instructions, see:
- **[DOCKER_SETUP_INTELLIDOCS.md](DOCKER_SETUP_INTELLIDOCS.md)** - Complete guide with all features
- **[docker/README_INTELLIDOCS.md](docker/README_INTELLIDOCS.md)** - Docker-specific documentation
## Standard Deployment
The easiest way to deploy paperless is `docker compose`. The files in the [`/docker/compose` directory](https://github.com/paperless-ngx/paperless-ngx/tree/main/docker/compose) are configured to pull the image from the GitHub container registry.
If you'd like to jump right in, you can configure a `docker compose` environment with our install script:

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# 🐳 IntelliDocs Docker Files
Este directorio contiene todos los archivos necesarios para ejecutar IntelliDocs usando Docker.
## 📁 Estructura
```
docker/
├── compose/ # Docker Compose configurations
│ ├── docker-compose.env # Plantilla de variables de entorno (ACTUALIZADA)
│ ├── docker-compose.intellidocs.yml # NUEVO: Compose optimizado para IntelliDocs
│ ├── docker-compose.sqlite.yml # SQLite (más simple)
│ ├── docker-compose.postgres.yml # PostgreSQL (producción)
│ ├── docker-compose.mariadb.yml # MariaDB
│ └── docker-compose.*-tika.yml # Con Apache Tika para OCR adicional
├── rootfs/ # Sistema de archivos raíz del contenedor
├── test-intellidocs-features.sh # NUEVO: Script de test para nuevas funciones
├── management_script.sh # Scripts de gestión
└── README_INTELLIDOCS.md # Este archivo
```
## 🚀 Inicio Rápido
### Opción 1: Usando el nuevo compose file optimizado (RECOMENDADO)
```bash
cd docker/compose
# Copiar y configurar variables de entorno
cp docker-compose.env docker-compose.env.local
nano docker-compose.env.local
# Crear directorios necesarios
mkdir -p data media export consume ml_cache
# Iniciar IntelliDocs con todas las nuevas funciones
docker compose -f docker-compose.intellidocs.yml up -d
# Ver logs
docker compose -f docker-compose.intellidocs.yml logs -f
```
### Opción 2: Usando compose files existentes
```bash
cd docker/compose
# Con SQLite (más simple)
docker compose -f docker-compose.sqlite.yml up -d
# Con PostgreSQL (recomendado para producción)
docker compose -f docker-compose.postgres.yml up -d
# Con MariaDB
docker compose -f docker-compose.mariadb.yml up -d
```
## ✅ Verificar Instalación
### Ejecutar script de test
```bash
cd docker
./test-intellidocs-features.sh
```
Este script verifica:
- ✓ Contenedores en ejecución
- ✓ Dependencias Python (torch, transformers, opencv, etc.)
- ✓ Módulos ML/OCR instalados
- ✓ Conexión a Redis
- ✓ Webserver respondiendo
- ✓ Variables de entorno configuradas
- ✓ Caché de modelos ML
## 🔧 Nuevas Funciones Disponibles
### Compose File Optimizado (`docker-compose.intellidocs.yml`)
Características especiales:
- ✨ **Redis optimizado** para caché con política LRU
- ✨ **Volumen ML cache** persistente para modelos
- ✨ **Health checks** mejorados
- ✨ **Resource limits** configurados para ML
- ✨ **Variables de entorno** pre-configuradas para nuevas funciones
- ✨ **Soporte GPU** (comentado, fácil de activar)
### Variables de Entorno Nuevas
En `docker-compose.env`:
```bash
# Habilitar funciones ML
PAPERLESS_ENABLE_ML_FEATURES=1
# Habilitar OCR avanzado
PAPERLESS_ENABLE_ADVANCED_OCR=1
# Modelo ML a usar
PAPERLESS_ML_CLASSIFIER_MODEL=distilbert-base-uncased
# Usar GPU (requiere NVIDIA Docker)
PAPERLESS_USE_GPU=0
# Umbral para detección de tablas
PAPERLESS_TABLE_DETECTION_THRESHOLD=0.7
# Reconocimiento de manuscritos
PAPERLESS_ENABLE_HANDWRITING_OCR=1
```
## 📊 Comparación de Compose Files
| Característica | sqlite.yml | postgres.yml | intellidocs.yml |
|---------------|-----------|--------------|-----------------|
| Base de datos | SQLite | PostgreSQL | SQLite/Config |
| Redis básico | ✓ | ✓ | ✓ Optimizado |
| ML cache | ✗ | ✗ | ✓ Persistente |
| Health checks | Básico | Básico | ✓ Completo |
| Resource limits | ✗ | ✗ | ✓ Configurado |
| GPU ready | ✗ | ✗ | ✓ Preparado |
| Variables ML | ✗ | ✗ | ✓ Pre-config |
## 🏗️ Construir Imagen Local
Si necesitas modificar el código o construir tu propia imagen:
```bash
# Desde la raíz del proyecto
cd ..
docker build -t intellidocs-ngx:dev .
# Luego modificar docker-compose.intellidocs.yml para usar imagen local:
# image: intellidocs-ngx:dev
```
## 🔍 Comandos Útiles
### Gestión de contenedores
```bash
cd docker/compose
# Ver estado
docker compose -f docker-compose.intellidocs.yml ps
# Ver logs
docker compose -f docker-compose.intellidocs.yml logs -f webserver
# Reiniciar
docker compose -f docker-compose.intellidocs.yml restart
# Detener
docker compose -f docker-compose.intellidocs.yml down
# Detener y eliminar volúmenes (¡CUIDADO! Borra datos)
docker compose -f docker-compose.intellidocs.yml down -v
```
### Acceso al contenedor
```bash
# Shell en webserver
docker compose -f docker-compose.intellidocs.yml exec webserver bash
# Ejecutar comando de Django
docker compose -f docker-compose.intellidocs.yml exec webserver python manage.py <command>
# Crear superusuario
docker compose -f docker-compose.intellidocs.yml exec webserver python manage.py createsuperuser
```
### Debugging
```bash
# Ver recursos
docker stats
# Inspeccionar volúmenes
docker volume ls
docker volume inspect docker_ml_cache
# Ver tamaño de caché ML
docker compose -f docker-compose.intellidocs.yml exec webserver du -sh /usr/src/paperless/.cache/
```
## 📦 Volúmenes
### Volúmenes Originales
- `data`: Base de datos y configuración
- `media`: Documentos procesados
- `export`: Exportaciones
- `consume`: Documentos a procesar
### Volúmenes Nuevos (IntelliDocs)
- `ml_cache`: **NUEVO** - Caché de modelos ML (~500MB-1GB)
- Persiste modelos descargados entre reinicios
- Primera descarga puede tomar 5-10 minutos
- Ubicación: `/usr/src/paperless/.cache/huggingface/`
## 🔧 Configuración Avanzada
### Activar Soporte GPU
1. Instalar NVIDIA Container Toolkit
2. En `docker-compose.intellidocs.yml`, descomentar:
```yaml
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
```
3. Configurar: `PAPERLESS_USE_GPU=1`
### Ajustar Memoria
Para sistemas con menos RAM:
```yaml
deploy:
resources:
limits:
memory: 4G # Reducir de 8G
reservations:
memory: 2G # Reducir de 4G
```
Y configurar workers:
```bash
PAPERLESS_TASK_WORKERS=1
PAPERLESS_THREADS_PER_WORKER=1
```
### Usar Base de Datos Externa
Modificar `docker-compose.intellidocs.yml` para usar PostgreSQL externo:
```yaml
environment:
PAPERLESS_DBHOST: your-postgres-host
PAPERLESS_DBPORT: 5432
PAPERLESS_DBNAME: paperless
PAPERLESS_DBUSER: paperless
PAPERLESS_DBPASS: your-password
```
## 📚 Documentación Adicional
- **Guía completa**: `/DOCKER_SETUP_INTELLIDOCS.md`
- **Bitácora del proyecto**: `/BITACORA_MAESTRA.md`
- **Funciones implementadas**:
- Fase 1: `/FASE1_RESUMEN.md` (Performance)
- Fase 2: `/FASE2_RESUMEN.md` (Security)
- Fase 3: `/FASE3_RESUMEN.md` (AI/ML)
- Fase 4: `/FASE4_RESUMEN.md` (Advanced OCR)
## 🐛 Troubleshooting
### Problema: Modelos ML no se descargan
```bash
# Verificar conectividad
docker compose -f docker-compose.intellidocs.yml exec webserver ping -c 3 huggingface.co
# Descargar manualmente
docker compose -f docker-compose.intellidocs.yml exec webserver python -c "
from transformers import AutoTokenizer, AutoModel
model = 'distilbert-base-uncased'
AutoTokenizer.from_pretrained(model)
AutoModel.from_pretrained(model)
"
```
### Problema: Out of Memory
```bash
# Reducir workers en docker-compose.env.local
PAPERLESS_TASK_WORKERS=1
PAPERLESS_THREADS_PER_WORKER=1
# Aumentar memoria de Docker Desktop
# Settings → Resources → Memory → 8GB+
```
### Problema: Permisos de archivos
```bash
# Ajustar permisos
sudo chown -R 1000:1000 ./data ./media ./consume ./export ./ml_cache
# O configurar UID/GID
USERMAP_UID=$(id -u)
USERMAP_GID=$(id -g)
```
## 🎯 Próximos Pasos
1. ✅ Configurar variables de entorno
2. ✅ Ejecutar `docker-compose.intellidocs.yml`
3. ✅ Ejecutar test script
4. ✅ Crear superusuario
5. ✅ Subir documentos de prueba
6. ✅ Verificar funciones ML/OCR
---
**IntelliDocs** - Sistema de Gestión Documental con IA
Versión: 1.0.0
Última actualización: 2025-11-09

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@ -1,5 +1,5 @@
###############################################################################
# Paperless-ngx settings #
# IntelliDocs (Paperless-ngx) settings #
###############################################################################
# See http://docs.paperless-ngx.com/configuration/ for all available options.
@ -13,15 +13,15 @@
# See the documentation linked above for all options. A few commonly adjusted settings
# are provided below.
# This is required if you will be exposing Paperless-ngx on a public domain
# This is required if you will be exposing IntelliDocs on a public domain
# (if doing so please consider security measures such as reverse proxy)
#PAPERLESS_URL=https://paperless.example.com
#PAPERLESS_URL=https://intellidocs.example.com
# Adjust this key if you plan to make paperless available publicly. It should
# be a very long sequence of random characters. You don't need to remember it.
#PAPERLESS_SECRET_KEY=change-me
# Use this variable to set a timezone for the Paperless Docker containers. Defaults to UTC.
# Use this variable to set a timezone for the Docker containers. Defaults to UTC.
#PAPERLESS_TIME_ZONE=America/Los_Angeles
# The default language to use for OCR. Set this to the language most of your
@ -35,3 +35,35 @@
# See https://packages.debian.org/search?keywords=tesseract-ocr-&searchon=names
# for available languages.
#PAPERLESS_OCR_LANGUAGES=tur ces
###############################################################################
# IntelliDocs Advanced ML/OCR Features (NEW) #
###############################################################################
# Enable/disable advanced ML features (BERT classification, NER, semantic search)
# Set to 1 to enable, 0 to disable. Default: 1 (enabled)
#PAPERLESS_ENABLE_ML_FEATURES=1
# Enable/disable advanced OCR features (table extraction, handwriting, forms)
# Set to 1 to enable, 0 to disable. Default: 1 (enabled)
#PAPERLESS_ENABLE_ADVANCED_OCR=1
# ML Model selection for document classification
# Options: distilbert-base-uncased (default, fast), bert-base-uncased (more accurate but slower)
#PAPERLESS_ML_CLASSIFIER_MODEL=distilbert-base-uncased
# Enable GPU acceleration for ML/OCR if available
# Set to 1 to use GPU, 0 to use CPU only. Default: 0 (CPU)
#PAPERLESS_USE_GPU=0
# Confidence threshold for table detection (0.0 to 1.0)
# Higher values = fewer false positives but might miss some tables. Default: 0.7
#PAPERLESS_TABLE_DETECTION_THRESHOLD=0.7
# Enable handwriting recognition for documents
# Set to 1 to enable, 0 to disable. Default: 1 (enabled)
#PAPERLESS_ENABLE_HANDWRITING_OCR=1
# Cache directory for ML models (to persist downloaded models between container restarts)
# Should be mounted as a volume for better performance
#PAPERLESS_ML_MODEL_CACHE=/usr/src/paperless/.cache/huggingface

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@ -0,0 +1,117 @@
# Docker Compose file for IntelliDocs with ML/OCR features
# This file is optimized for the new AI/ML and Advanced OCR capabilities
#
# IntelliDocs includes:
# - Phase 1: Performance optimizations (147x faster)
# - Phase 2: Security hardening (A+ security score)
# - Phase 3: AI/ML features (BERT classification, NER, semantic search)
# - Phase 4: Advanced OCR (table extraction, handwriting, form detection)
#
# Hardware Requirements:
# - CPU: 4+ cores recommended
# - RAM: 8GB minimum, 16GB recommended for ML features
# - Disk: 20GB+ (includes ML models cache)
#
# To deploy:
#
# 1. Copy docker-compose.env to docker-compose.env.local and configure
# 2. Create required directories:
# mkdir -p ./data ./media ./export ./consume ./ml_cache
# 3. Run: docker compose -f docker-compose.intellidocs.yml up -d
#
# For more details, see: DOCKER_SETUP_INTELLIDOCS.md
services:
broker:
image: docker.io/library/redis:8
restart: unless-stopped
volumes:
- redisdata:/data
# Redis configuration for better performance with caching
command: >
redis-server
--maxmemory 512mb
--maxmemory-policy allkeys-lru
--save 60 1000
healthcheck:
test: ["CMD", "redis-cli", "ping"]
interval: 30s
timeout: 10s
retries: 3
start_period: 30s
webserver:
image: ghcr.io/paperless-ngx/paperless-ngx:latest
# To build locally instead:
# build:
# context: ../..
# dockerfile: Dockerfile
restart: unless-stopped
depends_on:
broker:
condition: service_healthy
ports:
- "8000:8000"
volumes:
# Core data volumes
- data:/usr/src/paperless/data
- media:/usr/src/paperless/media
- ./export:/usr/src/paperless/export
- ./consume:/usr/src/paperless/consume
# ML models cache (IMPORTANT: persists downloaded models)
- ml_cache:/usr/src/paperless/.cache
env_file: docker-compose.env
environment:
PAPERLESS_REDIS: redis://broker:6379
# Enable new features by default
PAPERLESS_ENABLE_ML_FEATURES: ${PAPERLESS_ENABLE_ML_FEATURES:-1}
PAPERLESS_ENABLE_ADVANCED_OCR: ${PAPERLESS_ENABLE_ADVANCED_OCR:-1}
# ML configuration
PAPERLESS_ML_CLASSIFIER_MODEL: ${PAPERLESS_ML_CLASSIFIER_MODEL:-distilbert-base-uncased}
PAPERLESS_USE_GPU: ${PAPERLESS_USE_GPU:-0}
# OCR configuration
PAPERLESS_TABLE_DETECTION_THRESHOLD: ${PAPERLESS_TABLE_DETECTION_THRESHOLD:-0.7}
PAPERLESS_ENABLE_HANDWRITING_OCR: ${PAPERLESS_ENABLE_HANDWRITING_OCR:-1}
# Model cache location
PAPERLESS_ML_MODEL_CACHE: /usr/src/paperless/.cache/huggingface
# Performance settings (adjust based on available RAM)
PAPERLESS_TASK_WORKERS: ${PAPERLESS_TASK_WORKERS:-2}
PAPERLESS_THREADS_PER_WORKER: ${PAPERLESS_THREADS_PER_WORKER:-2}
healthcheck:
test: ["CMD", "curl", "-fs", "-S", "-L", "--max-time", "2", "http://localhost:8000"]
interval: 30s
timeout: 10s
retries: 5
start_period: 120s # ML models may take time to load on first start
# Resource limits (adjust based on your system)
deploy:
resources:
limits:
memory: 8G # Increase for larger ML models
reservations:
memory: 4G # Minimum for ML features
# Uncomment below for GPU support (requires nvidia-container-toolkit)
# deploy:
# resources:
# reservations:
# devices:
# - driver: nvidia
# count: 1
# capabilities: [gpu]
volumes:
data:
driver: local
media:
driver: local
redisdata:
driver: local
ml_cache:
driver: local
# Important: This volume persists ML models between container restarts
# First run will download ~500MB-1GB of models
# Network configuration (optional)
# networks:
# default:
# name: intellidocs_network

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@ -0,0 +1,195 @@
#!/bin/bash
# Test script for IntelliDocs new features in Docker
# This script verifies that all ML/OCR dependencies and features are working
set -e
echo "=========================================="
echo "IntelliDocs Feature Test Script"
echo "=========================================="
echo ""
# Colors for output
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
NC='\033[0m' # No Color
# Check if docker compose is available
if ! command -v docker &> /dev/null; then
echo -e "${RED}✗ Docker is not installed${NC}"
exit 1
fi
echo -e "${GREEN}✓ Docker is installed${NC}"
# Check if compose file exists
COMPOSE_FILE="compose/docker-compose.intellidocs.yml"
if [ ! -f "$COMPOSE_FILE" ]; then
echo -e "${RED}✗ Compose file not found: $COMPOSE_FILE${NC}"
exit 1
fi
echo -e "${GREEN}✓ Docker compose file found${NC}"
echo ""
# Test 1: Check if containers are running
echo "Test 1: Checking if containers are running..."
if docker compose -f "$COMPOSE_FILE" ps | grep -q "Up"; then
echo -e "${GREEN}✓ Containers are running${NC}"
else
echo -e "${YELLOW}! Containers are not running. Starting them...${NC}"
docker compose -f "$COMPOSE_FILE" up -d
echo "Waiting 60 seconds for containers to initialize..."
sleep 60
fi
echo ""
# Test 2: Check Python dependencies
echo "Test 2: Checking ML/OCR Python dependencies..."
docker compose -f "$COMPOSE_FILE" exec -T webserver python3 << 'PYTHON_EOF'
import sys
errors = []
success = []
# Test torch
try:
import torch
success.append(f"torch {torch.__version__}")
except ImportError as e:
errors.append(f"torch: {str(e)}")
# Test transformers
try:
import transformers
success.append(f"transformers {transformers.__version__}")
except ImportError as e:
errors.append(f"transformers: {str(e)}")
# Test OpenCV
try:
import cv2
success.append(f"opencv {cv2.__version__}")
except ImportError as e:
errors.append(f"opencv: {str(e)}")
# Test sentence-transformers
try:
import sentence_transformers
success.append(f"sentence-transformers {sentence_transformers.__version__}")
except ImportError as e:
errors.append(f"sentence-transformers: {str(e)}")
# Test pandas
try:
import pandas
success.append(f"pandas {pandas.__version__}")
except ImportError as e:
errors.append(f"pandas: {str(e)}")
# Test numpy
try:
import numpy
success.append(f"numpy {numpy.__version__}")
except ImportError as e:
errors.append(f"numpy: {str(e)}")
# Test PIL
try:
from PIL import Image
success.append("pillow (PIL)")
except ImportError as e:
errors.append(f"pillow: {str(e)}")
# Test pytesseract
try:
import pytesseract
success.append("pytesseract")
except ImportError as e:
errors.append(f"pytesseract: {str(e)}")
for s in success:
print(f"✓ {s}")
if errors:
print("\nErrors:")
for e in errors:
print(f"✗ {e}")
sys.exit(1)
else:
print("\n✓ All dependencies installed correctly!")
sys.exit(0)
PYTHON_EOF
if [ $? -eq 0 ]; then
echo -e "${GREEN}✓ All Python dependencies are available${NC}"
else
echo -e "${RED}✗ Some Python dependencies are missing${NC}"
exit 1
fi
echo ""
# Test 3: Check if ML modules exist
echo "Test 3: Checking ML/OCR module files..."
for module in "documents/ml/classifier.py" "documents/ml/ner.py" "documents/ml/semantic_search.py" "documents/ocr/table_extractor.py" "documents/ocr/handwriting.py" "documents/ocr/form_detector.py"; do
if docker compose -f "$COMPOSE_FILE" exec -T webserver test -f "/usr/src/paperless/src/$module"; then
echo -e "${GREEN}$module exists${NC}"
else
echo -e "${RED}$module not found${NC}"
exit 1
fi
done
echo ""
# Test 4: Check Redis connection
echo "Test 4: Checking Redis connection..."
if docker compose -f "$COMPOSE_FILE" exec -T broker redis-cli ping | grep -q "PONG"; then
echo -e "${GREEN}✓ Redis is responding${NC}"
else
echo -e "${RED}✗ Redis is not responding${NC}"
exit 1
fi
echo ""
# Test 5: Check if webserver is responding
echo "Test 5: Checking if webserver is responding..."
if docker compose -f "$COMPOSE_FILE" exec -T webserver curl -f -s http://localhost:8000 > /dev/null; then
echo -e "${GREEN}✓ Webserver is responding${NC}"
else
echo -e "${YELLOW}! Webserver is not responding yet (may still be initializing)${NC}"
fi
echo ""
# Test 6: Check environment variables
echo "Test 6: Checking ML/OCR environment variables..."
docker compose -f "$COMPOSE_FILE" exec -T webserver bash << 'BASH_EOF'
echo "PAPERLESS_ENABLE_ML_FEATURES=${PAPERLESS_ENABLE_ML_FEATURES:-not set}"
echo "PAPERLESS_ENABLE_ADVANCED_OCR=${PAPERLESS_ENABLE_ADVANCED_OCR:-not set}"
echo "PAPERLESS_ML_CLASSIFIER_MODEL=${PAPERLESS_ML_CLASSIFIER_MODEL:-not set}"
echo "PAPERLESS_USE_GPU=${PAPERLESS_USE_GPU:-not set}"
BASH_EOF
echo ""
# Test 7: Check ML model cache
echo "Test 7: Checking ML model cache..."
docker compose -f "$COMPOSE_FILE" exec -T webserver ls -lah /usr/src/paperless/.cache/ || echo -e "${YELLOW}! ML cache directory may not be initialized yet${NC}"
echo ""
# Test 8: Check system resources
echo "Test 8: Checking system resources..."
docker stats --no-stream --format "table {{.Name}}\t{{.CPUPerc}}\t{{.MemUsage}}" $(docker compose -f "$COMPOSE_FILE" ps -q)
echo ""
echo "=========================================="
echo -e "${GREEN}✓ All tests completed successfully!${NC}"
echo "=========================================="
echo ""
echo "Next steps:"
echo "1. Access IntelliDocs at: http://localhost:8000"
echo "2. Create a superuser: docker compose -f $COMPOSE_FILE exec webserver python manage.py createsuperuser"
echo "3. Upload a test document to try the new ML/OCR features"
echo "4. Check logs: docker compose -f $COMPOSE_FILE logs -f webserver"
echo ""
echo "For more information, see: DOCKER_SETUP_INTELLIDOCS.md"
echo ""