https://github.com/linode/ai-quickstart-n8n
https://github.com/linode/ai-quickstart-n8n
Last synced: 24 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/linode/ai-quickstart-n8n
- Owner: linode
- License: apache-2.0
- Created: 2025-12-17T13:23:40.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-12-19T12:52:14.000Z (6 months ago)
- Last Synced: 2026-02-11T01:36:16.034Z (4 months ago)
- Language: Shell
- Size: 35.2 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Akamai Cloud - AI Quickstart : n8n AI Workflow Automation
Automated deployment script to run your private, self-hosted AI workflow automation platform on Akamai Cloud GPU instances. This stack combines vLLM for high-performance LLM inference with n8n - a powerful workflow automation tool that lets you build AI-powered automations with a visual editor.
-----------------------------------------
## 🚀 Quick Start
Just run this single command:
```bash
curl -fsSL https://raw.githubusercontent.com/linode/ai-quickstart-n8n/main/deploy.sh | bash
```
That's it! The script will download required files and guide you through the interactive deployment process.
## ✨ Features
- **Fully Automated Deployment**: Handles instance creation with real-time progress tracking
- **Ready to use AI Stack**: vLLM for GPU-accelerated inference + n8n for AI workflow automation
- **Visual Workflow Builder**: Create complex AI workflows with n8n's drag-and-drop interface
- **OpenAI-Compatible API**: Use vLLM as a drop-in replacement for OpenAI in your workflows
- **Cross-Platform Support**: Works on macOS, Linux, and Windows (Git Bash/WSL)
-----------------------------------------
## 🏗️ What Gets Deployed
### Linode GPU Instance with
- Ubuntu 24.04 LTS with NVIDIA drivers
- Docker & NVIDIA Container Toolkit
- Systemd service for automatic startup on reboot
### Docker Containers
| | Service | Description |
|:--:|:--|:--|
| 
| **Caddy** | Reverse proxy with automatic HTTPS (port 80/443) |
| 
| **n8n** | AI workflow automation platform with visual editor (port 5678, internal) |
| 
| **vLLM** | High-throughput LLM inference engine with OpenAI-compatible API (port 8000, internal) |
| | **Embedding** | Text embedding model for vector search (BAAI/bge-m3, port 8001, internal) |
| | **Reranker** | Semantic reranking model (BAAI/bge-reranker-v2-m3, port 8002, internal) |
| 
| **pgvector** | PostgreSQL database with vector extension for n8n (port 5432, internal) |
### Models
#### LLM: openai/gpt-oss-20b
[gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) is OpenAI's first fully open-source LLM, released under Apache 2.0 license. Key characteristics:
- **20B parameters**: Fits on a single RTX 4000 Ada GPU (20GB VRAM)
- **High benchmark scores**: Competitive with larger models on reasoning and instruction-following tasks
- **High throughput**: Optimized for fast token generation with vLLM inference engine
#### Embedding: BAAI/bge-m3
[BGE-M3](https://huggingface.co/BAAI/bge-m3) is a multilingual embedding model for semantic search and RAG applications:
- **Multilingual support**: 100+ languages
- **Multi-functionality**: Dense retrieval, sparse retrieval, and multi-vector retrieval
- **Optimized for RAG**: Perfect for n8n AI workflows requiring vector search
#### Reranker: BAAI/bge-reranker-v2-m3
[BGE Reranker V2](https://huggingface.co/BAAI/bge-reranker-v2-m3) improves search result relevance:
- **Semantic reranking**: Reorders search results by semantic similarity
- **Lightweight**: Efficient model for real-time reranking
- **Multilingual**: Supports cross-lingual reranking tasks
### What is n8n?
[n8n](https://n8n.io/) is a powerful workflow automation tool that lets you connect anything to everything. Key features include:
- **Visual Workflow Builder**: Create complex automations with a drag-and-drop interface
- **400+ Integrations**: Connect to databases, APIs, SaaS tools, and more
- **AI Capabilities**: Built-in AI nodes for LLM integration, embeddings, and vector stores
- **Self-Hosted**: Full control over your data and workflows - nothing leaves your server
- **PostgreSQL Backend**: Production-ready database with pgvector extension for AI workflows
-----------------------------------------
## 📋 Requirements
### Akamai Cloud Account
- Active Linode account with GPU access enabled
### Local System Requirements
- **Required**: bash, curl, ssh, jq
- **Note**: jq will be auto-installed if missing
-----------------------------------------
## 🚦 Getting Started
### 1. Option A: Single Command Execution
No installation required - just run:
```bash
curl -fsSL https://raw.githubusercontent.com/linode/ai-quickstart-n8n/main/deploy.sh | bash
```
### 1. Option B: Download and Run
Download the script and run locally:
```bash
curl -fsSLO https://raw.githubusercontent.com/linode/ai-quickstart-n8n/main/deploy.sh
bash deploy.sh
```
### 1. Option C: Clone Repository
If you prefer to inspect or customize the scripts:
```bash
git clone https://github.com/linode/ai-quickstart-n8n
cd ai-quickstart-n8n
./deploy.sh
```
> [!NOTE]
> if you like to add more services check out docker compose template file
> ```
> vi /template/docker-compose.yml
> ```
>
### 2. Follow Interactive Prompts
The script will ask you to:
- Choose a region (e.g., us-east, eu-west)
- Select GPU instance type
- Provide instance label
- Select or generate SSH keys
- Confirm deployment
### 3. Wait for Deployment
The script automatically:
- Creates GPU instance in your Linode account
- Monitors cloud-init installation progress
- Waits for n8n health check
- Waits for vLLM model loading
### 4. Access Your Services
Once complete, you'll see:
```
🎉 Setup Complete!
✅ Your AI LLM instance is now running!
🌐 Access URLs:
n8n: https://.ip.linodeusercontent.com
🔐 Access Credentials:
SSH: ssh -i /path/to/your/key root@
```
### Configuration files in GPU Instance
```
# Bootstrap script called by cloud-init service
/opt/ai-quickstart-n8n/bootstrap.sh
# docker compose file called by systemctl at startup
/opt/ai-quickstart-n8n/docker-compose.yml
# Caddy reverse proxy configuration
/opt/ai-quickstart-n8n/Caddyfile
# service definition
/etc/systemd/system/ai-quickstart-n8n.service
```
-----------------------------------------
## 🗑️ Delete Instance
To delete a deployed instance:
```bash
# Remote execution
curl -fsSL https://raw.githubusercontent.com/linode/ai-quickstart-n8n/main/delete.sh | bash -s --
# Or download script and run
curl -fsSLO https://raw.githubusercontent.com/linode/ai-quickstart-n8n/main/delete.sh
bash delete.sh
```
The script will show instance details and ask for confirmation before deletion.
-----------------------------------------
## 📁 Project Structure
```
ai-quickstart-n8n/
├── deploy.sh # Main deployment script
├── delete.sh # Instance deletion script
├── template/
│ ├── cloud-init.yaml # Cloud-init configuration
│ └── bootstrap.sh # Post-boot installation script
└── setup/
├── docker-compose.yml # Docker Compose configuration
├── Caddyfile # Caddy reverse proxy configuration
├── setup.sh # Setup script run by systemd
├── n8n_credentials.json # n8n credentials template
└── n8n_workflow.json # n8n workflow template
```
-----------------------------------------
## 🔒 Security
**⚠️ IMPORTANT**: By default, ports 80 and 443 are exposed to the internet
### Immediate Security Steps
1. **Configure Cloud Firewall** (Recommended)
- Create Linode Cloud Firewall
- Restrict access to ports 80/443 by source IP
- Allow SSH (port 22) from trusted IPs only
2. **SSH Security**
- SSH key authentication required
- Root password provided for emergency console access only
-----------------------------------------
## 🛠️ Useful Commands
```bash
# SSH into your instance
ssh -i /path/to/your/key root@
# Check container status
docker ps -a
# Check Docker containers log
cd /opt/ai-quickstart-n8n && docker compose logs -f
# Check systemd service status
systemctl status ai-quickstart-n8n.service
# View systemd service logs
journalctl -u ai-quickstart-n8n.service -n 100
# Check cloud-init logs
tail -f /var/log/cloud-init-output.log -n 100
# Restart all services
systemctl restart ai-quickstart-n8n.service
# Check NVIDIA GPU status
nvidia-smi
# Check vLLM loaded models
curl http://localhost:8000/v1/models
# Check embedding model
curl http://localhost:8001/v1/models
# Check reranker model
curl http://localhost:8002/v1/models
# Check n8n health
curl http://localhost:5678/healthz
# Check PostgreSQL database
docker exec -it pgvector psql -U n8n -d n8n -c "SELECT version();"
# Check container logs
docker logs vllm
docker logs embedding
docker logs reranker
docker logs n8n
docker logs pgvector
```
## 🤝 Contributing
Issues and pull requests are welcome! For major changes, please open an issue first to discuss what you would like to change.
## 📄 License
This project is licensed under the Apache License 2.0.