100% local Retrieval-Augmented Generation (RAG) system for businesses that need complete data privacy and control.

One command. Full RAG system. Your data stays yours.

• ✅ 100% Local: No data leaves your infrastructure
• 🚀 One-Command Setup: Automated installation script (~1 hour / ~15 min with fast connection)
• 🤖 Modern LLMs: Qwen3, Mistral 7B (Q4 quantized)
• 🔐 Multi-user Auth: JWT-based with role-based access control
• 💾 Backup & Restore: Full system backup with 70+ cloud providers via rclone
• 📊 Production-Ready: Designed for 10,000+ documents
• 🌍 Multilingual: Supports 29 languages
• 🎨 Clean UI: Modern React interface
• 📁 Multiple Formats: PDF, DOCX, TXT, MD, PPTX, XLSX, and more

• OS: Ubuntu 20.04+ (22.04 recommended)
• GPU: NVIDIA (CUDA), AMD (ROCm), or CPU-only
  • NVIDIA: 8-16GB VRAM, drivers pre-installed
  • AMD: ROCm-compatible GPU, ROCm drivers pre-installed
  • CPU: No GPU required (slower inference)
• RAM: 16GB minimum, 32GB recommended
• Storage: 50GB+ available
• Connection: 80+ Mbit/s recommended

# 1. Clone repository
git clone https://github.com/I3K-IT/RAG-Enterprise.git
cd RAG-Enterprise/rag-enterprise-structure

# 2. Run setup script (installs everything)
./setup.sh standard

# 3. Follow prompts - you'll need to logout/login once during setup
# Setup time: ~1 hour (80 Mbit/s) / ~10-15 min (400+ Mbit/s)

# 4. Access the application
# Frontend: http://localhost:3000
# Get admin password: docker compose logs backend | grep "Password:"

The setup script automatically installs and configures:

• Docker Engine + Docker Compose
• GPU toolkit (NVIDIA Container Toolkit or AMD ROCm, based on your selection)
• All required Docker images (CUDA or ROCm variant)
• Ollama with LLM model (Mistral 7B or Qwen3:14b)
• Qdrant vector database
• Backend + Frontend services

First Startup: After setup completes, the backend downloads the embedding model (~2.3GB) on first startup. This takes ~9 minutes (80 Mbit/s) or ~2 minutes (400+ Mbit/s). Check status with:

docker compose logs backend -f

Once you see "Application startup complete", open http://localhost:3000 and login.

┌─────────────────────────────────────────┐
│  React + Vite Frontend (Port 3000)     │
│  - JWT Authentication                   │
│  - Document Management                  │
│  - Conversation History                 │
└─────────────────┬───────────────────────┘
                  │ REST API
                  ↓
┌─────────────────────────────────────────┐
│  FastAPI Backend (Port 8000)           │
│  - RAG Pipeline (LangChain)            │
│  - Role-Based Access Control           │
│  - Backup & Restore (rclone)           │
│  - OCR (Apache Tika + Tesseract)       │
│  - Embeddings (BAAI/bge-m3)            │
└─────────────────┬───────────────────────┘
                  │
    ┌─────────────┴─────────────┐
    ↓                           ↓
┌─────────────────┐   ┌─────────────────┐
│  Qdrant (6333) │   │  Ollama (11434) │
│  Vector DB     │   │  LLM Server     │
└─────────────────┘   └─────────────────┘

• User: Query documents (read-only)
• Super User: Upload and delete documents
• Admin: Full access including user management

1. Open http://localhost:3000
2. Get your admin password from the logs:

   docker compose logs backend | grep "Password:"

3. Login with username admin and the password from logs
4. Create additional users in Admin panel

Note: The admin password is randomly generated at first startup for security. It is not a fixed default like admin123. If you don't see it in the logs, see Password Recovery below.

1. Login as Super User or Admin
2. Click "Upload Document"
3. Select files (PDF, DOCX, TXT, MD, etc.)
4. Wait for processing (1-2 min per document)
5. Start querying your documents

• ✅ PDF (with OCR)
• ✅ DOCX/DOC
• ✅ PPTX/PPT
• ✅ XLSX/XLS
• ✅ TXT, MD
• ✅ ODT, RTF, HTML, XML

RAG Enterprise includes a complete backup system that protects your entire deployment — database, documents, and vector store — with support for 70+ cloud providers via rclone.

• One-click backup: Create full system backups from the admin panel
• 10 provider types: Mega, S3/MinIO, Google Drive, OneDrive, Dropbox, WebDAV (Nextcloud), FTP, SFTP, Backblaze B2, pCloud
• Automatic scheduling: Cron-based with configurable retention policies
• Selective restore: Choose which components to restore (database, documents, vectors)
• Zero-downtime backup: SQLite safe backup API, no service interruption

# Add a Mega cloud provider
curl -X POST http://localhost:8000/api/admin/backup/providers \
  -H "Authorization: Bearer TOKEN" \
  -d '{"name": "my-mega", "type": "mega", "config": {"user": "email@example.com", "pass": "password"}}'

# Run backup + upload to cloud
curl -X POST http://localhost:8000/api/admin/backup/run \
  -H "Authorization: Bearer TOKEN" \
  -d '{"provider": "my-mega"}'

# Schedule daily backups at 2 AM
curl -X POST http://localhost:8000/api/admin/backup/schedule \
  -H "Authorization: Bearer TOKEN" \
  -d '{"cron": "0 2 * * *", "provider": "my-mega", "retention": 5, "enabled": true}'

Full documentation: See docs/BACKUP.md for complete setup guide, all provider configurations, API reference, and troubleshooting.

Edit docker-compose.yml:

environment:
  LLM_MODEL: qwen3:14b-q4_K_M  # or mistral:7b-instruct-q4_K_M
  EMBEDDING_MODEL: BAAI/bge-m3
  RELEVANCE_THRESHOLD: "0.35"

Then restart:

docker compose restart backend

See LOGO_SETUP.md for logo and company name customization.

# View all logs
docker compose logs -f

# View backend logs only
docker compose logs -f backend

# Check service status
docker compose ps

# Restart services
docker compose restart

# Stop everything
docker compose down

# Start everything
docker compose up -d

# Health checks
curl http://localhost:8000/health
curl http://localhost:3000

If you need to start fresh:

# Complete cleanup (removes everything)
./cleanup.sh

# Logout and login again

# Run setup from scratch
./setup.sh standard

Wait 3-5 minutes on first startup - it's downloading the embedding model:

docker compose logs backend -f

Look for "Application startup complete" message.

The admin password is randomly generated on first startup. If the logs have been cleared and you can't find it:

Option A: Set a custom password and recreate the admin user:

# 1. Add to your .env file
echo "ADMIN_DEFAULT_PASSWORD=your-secure-password" >> .env

# 2. Delete the user database to force recreation
docker compose exec backend rm /app/data/rag_users.db

# 3. Restart the backend
docker compose restart backend

Option B: Set ADMIN_DEFAULT_PASSWORD in .env before first startup to avoid random passwords entirely:

# In .env - the admin account will use this password
ADMIN_DEFAULT_PASSWORD=your-secure-password

Tip: If you prefer a known password, set ADMIN_DEFAULT_PASSWORD in .env before first startup. Otherwise, the system generates a secure random password shown only once in the logs.

Check all services are running:

docker compose ps

# All should show "Up" status
# If backend is "unhealthy", wait a few more minutes

NVIDIA:

nvidia-smi
docker run --rm --gpus all nvidia/cuda:12.9.0-runtime-ubuntu22.04 nvidia-smi

AMD (ROCm):

ls /dev/kfd /dev/dri
# Verify ROCm: https://rocm.docs.amd.com

CPU-only: No GPU needed — set GPU_TYPE=cpu in .env (or select option 3 during setup).

Lower the similarity threshold in docker-compose.yml:

RELEVANCE_THRESHOLD: "0.3"  # Lower = more results

Then docker compose restart backend.

• Setup time: ~1 hour (80 Mbit/s) / ~10-15 min (400+ Mbit/s)
• First startup: +9 min (80 Mbit/s) / +2 min (400+ Mbit/s) for embedding model
• Total ready: ~1h 10min (80 Mbit/s) / ~15-20 min (fast connection)
• Query response: 2-4 seconds
• Generation speed: 80-100 tokens/s
• Document capacity: 1,000-10,000 documents
• Upload speed: 1-2 documents/minute

Real-world performance results from the community. Run python benchmark/rag_benchmark.py to test your hardware and contribute your results!

Want to contribute? Run the benchmark on your hardware and open an issue with your results! See benchmark/README.md for instructions.

• ✅ Zero external calls: Everything runs locally
• ✅ No telemetry: No tracking or analytics
• ✅ Local models: LLM and embeddings on-premise
• ✅ AGPL-3.0 License: If you modify and deploy as a service, you must share source code

Ideal for: Law firms, healthcare, finance, government, enterprises with sensitive data.

If you use RAG Enterprise in academic research, EU grant proposals, or technical publications, please cite it as:

APA:

Marchetti, F. (2026). RAG Enterprise — Self-hosted document intelligence platform (Version 1.2.1) [Software]. Zenodo. https://doi.org/10.5281/zenodo.20413005

BibTeX:

@software{marchetti2026ragenterprise,
  author       = {Marchetti, Francesco},
  title        = {RAG Enterprise: Self-hosted document intelligence platform},
  year         = {2026},
  publisher    = {Zenodo},
  version      = {v1.2.1},
  doi          = {10.5281/zenodo.20413005},
  url          = {https://doi.org/10.5281/zenodo.20413005},
  license      = {AGPL-3.0},
  note         = {Sovereign GDPR-compliant Retrieval-Augmented Generation system for regulated industries; Community (AGPL-3.0) and Pro (commercial) editions}
}

Plain text:

Francesco Marchetti. (2026). RAG Enterprise — Self-hosted document intelligence platform (v1.2.1) [Software]. https://doi.org/10.5281/zenodo.20413005

This project is licensed under AGPL-3.0 - see LICENSE file.

What this means:

• ✅ Free to use and modify
• ✅ Must share modifications if you offer it as a service
• ✅ Protects against proprietary SaaS parasites
• ✅ Still fully open-source

We welcome contributions! Please read our guidelines:

• Contributing Guide - How to contribute
• Code of Conduct - Community guidelines
• Security Policy - Report vulnerabilities
• Roadmap - Planned features

Quick start:

1. Fork the repository
2. Create feature branch (git checkout -b feature/YourFeature)
3. Make your changes
4. Open Pull Request

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Docs: See LOGO_SETUP.md for branding, docs/BACKUP.md for backup guide

If you find this project useful, please consider giving it a star! It helps others discover the project.

Built with:

• Ollama - Local LLM runtime
• Qdrant - Vector database
• LangChain - RAG orchestration
• FastAPI - Backend framework
• React + Vite - Frontend
• Apache Tika - Document processing
• rclone - Cloud storage integration for backups

Francesco Marchetti - Creator & Lead Developer

• LinkedIn: francesco-marchetti
• Company: I3K Technologies
• Email: info@i3k.eu
• Phone: +39 02 99914444 | +39 338 7913579

Made with ❤️ by I3K Technologies for privacy-conscious enterprises