ellmos-stack

A self-hosted AI research and knowledge management stack. Combines a local LLM, workflow automation, persistent memory, and a knowledge base into one deployable setup.

Zero cloud dependencies. Everything runs on your own server.

What's inside

┌─────────────────────────────────────────────────────────┐
│                    ellmos-stack                          │
│                                                         │
│  ┌──────────┐  ┌──────────┐  ┌───────────────────────┐ │
│  │  Ollama   │  │   n8n    │  │  Research Pipeline    │ │
│  │ Local LLM │  │ Workflow │  │ PubMed/arXiv → Ollama │ │
│  │ (qwen3)  │  │  Engine  │  │    → KnowledgeDigest  │ │
│  └────┬─────┘  └────┬─────┘  └───────────┬───────────┘ │
│       │              │                     │             │
│  ┌────┴──────────────┴─────────────────────┴──────────┐ │
│  │              Shared Services                        │ │
│  │  ┌─────────────────┐  ┌──────────────────────────┐ │ │
│  │  │    Rinnsal       │  │   KnowledgeDigest        │ │ │
│  │  │  Memory + Tasks  │  │  Document Search + Web   │ │ │
│  │  │  Ollama Runner   │  │  Auto-Indexing + Summary │ │ │
│  │  └─────────────────┘  └──────────────────────────┘ │ │
│  └────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────┘

Component	Role	Source
Ollama	Local LLM inference (qwen3:4b default)	Docker
n8n	Workflow automation, webhooks, scheduling	Docker
Rinnsal	Lightweight memory + task management for AI agents	pip
KnowledgeDigest	Document ingestion, chunking, search, web UI	pip
Research Pipeline	Automated paper search → analysis → storage	included

Requirements

• Server: Linux (Ubuntu 22.04+, Debian 12+), 2+ CPU cores, 8+ GB RAM
• Software: Docker, Docker Compose v2, Python 3.10+
• Disk: ~5 GB for base setup (model + containers)

Tested on Hetzner CCX13 (2 vCPU, 8 GB RAM, ~18 EUR/month).

Quickstart

# Clone
git clone https://github.com/ellmos-ai/ellmos-stack.git
cd ellmos-stack

# Install (as root)
sudo ./install.sh

# That's it. Services are running:
#   n8n:              http://your-ip:5678
#   KnowledgeDigest:  http://your-ip:8787
#   Ollama:           localhost:11434 (internal)

The installer:

1. Installs system dependencies (Python, Git, curl)
2. Sets up Docker services (Ollama + n8n)
3. Pulls the configured LLM model
4. Installs Python components (Rinnsal, KnowledgeDigest)
5. Creates systemd service for KnowledgeDigest web viewer
6. Sets up cron jobs for auto-indexing and background summarization
7. Generates a secure n8n password (saved in .env)

Configuration

Copy and edit .env:

cp .env.example .env
nano .env

Key settings:

Variable	Default	Description
N8N_PASSWORD	(generated)	n8n web interface password
OLLAMA_MODEL	qwen3:4b	LLM model to use
OLLAMA_MEMORY_LIMIT	6G	Max RAM for Ollama
KD_PORT	8787	KnowledgeDigest web UI port
KD_SUMMARY_PROVIDER	ollama	Summary backend: ollama, anthropic

Use Cases

1. Knowledge Base

Drop documents (PDF, TXT, MD, DOCX) into the inbox directory:

cp paper.pdf /opt/ellmos-stack/data/knowledgedigest/inbox/
# Auto-indexed within 5 minutes, summaries generated within 15 minutes

Browse and search at http://your-ip:8787.

2. Research Automation

Search academic papers, analyze with your local LLM, store results:

cd /opt/ellmos-stack
venv/bin/python services/research_pipeline.py \
    "dark matter detection methods" \
    --papers 10 --summarize --save

3. AI Memory & Tasks

Persistent memory and task management for AI agents:

from rinnsal import memory, tasks

memory.init("/opt/ellmos-stack/data/rinnsal/rinnsal.db")
memory.write("Server setup completed", tags=["infra"])

tasks.init("/opt/ellmos-stack/data/rinnsal/rinnsal.db")
tasks.add("Review research results", priority="high")

4. Workflow Automation (n8n)

Build automated workflows with n8n at http://your-ip:5678:

• Scheduled research: Cron → Research Pipeline → Email digest
• Document processing: Webhook → Download → KnowledgeDigest inbox
• Monitoring: Health checks → Alerts

5. Direct LLM Access

Query Ollama directly from any service:

curl http://localhost:11434/api/generate \
    -d '{"model":"qwen3:4b","prompt":"Explain quantum entanglement briefly"}'

Or via Rinnsal's OllamaRunner:

from rinnsal.auto import OllamaRunner

runner = OllamaRunner(model="qwen3:4b", think=False)
result = runner.run("Summarize this text: ...")

6. Desktop Document Analysis (NoteSpaceLLM)

Use NoteSpaceLLM as a desktop client for interactive document analysis, powered by the stack's Ollama instance:

1. Install NoteSpaceLLM on your local machine
2. Set up an Ollama auth proxy (see Exposing Ollama below)
3. In NoteSpaceLLM: Menu > LLM > Settings > set your server URL and API key

NoteSpaceLLM provides drag-and-drop document analysis, RAG-based chat, and multi-format report export -- all processed by the stack's LLM.

Architecture

The stack uses Docker for Ollama and n8n (stateful services with volumes), and pip packages for the Python components (Rinnsal, KnowledgeDigest). Background processing runs via cron.

Port 5678  ──→ n8n (Docker)
Port 8787  ──→ KnowledgeDigest Web Viewer (systemd)
Port 11434 ──→ Ollama (Docker, localhost only)
Port 11435 ──→ Ollama Auth Proxy (Nginx, optional, for remote clients)

Cron:
  */5  min ──→ auto_ingest.py (index new documents)
  */15 min ──→ process_summaries.py (LLM summarization)
  */5  min ──→ ollama-service health (auto-restart if down)

Data is stored in /opt/ellmos-stack/data/ (SQLite databases, document files).

Customization

Different LLM model

# Edit .env
OLLAMA_MODEL=mistral:7b

# Pull the new model
docker exec ollama ollama pull mistral:7b

# For NoteSpaceLLM RAG embeddings, also pull an embedding model:
docker exec ollama ollama pull nomic-embed-text

# Restart summary processing (uses OLLAMA_MODEL from .env)

Cloud LLM for summaries

# In .env
KD_SUMMARY_PROVIDER=anthropic
ANTHROPIC_API_KEY=sk-ant-...

Custom system prompt

Edit config/system_prompt.txt to adjust the LLM's personality, language, and behavior.

Exposing Ollama for Remote Access

By default, Ollama only listens on localhost. To allow desktop clients (like NoteSpaceLLM) or other machines to use your stack's LLM, set up an Nginx reverse proxy with API key authentication:

# Install Nginx
apt install nginx

# Create proxy config
cat > /etc/nginx/sites-available/ollama-proxy << 'EOF'
server {
    listen 11435;
    server_name _;

    location / {
        if ($http_authorization != "Bearer YOUR_SECRET_API_KEY") {
            return 401 "Unauthorized";
        }
        proxy_pass http://127.0.0.1:11434;
        proxy_set_header Host $host;
        proxy_read_timeout 300s;
        proxy_buffering off;
    }

    # Unauthenticated health endpoint
    location /health {
        proxy_pass http://127.0.0.1:11434/api/tags;
        proxy_read_timeout 5s;
    }
}
EOF

# Enable and start
ln -sf /etc/nginx/sites-available/ollama-proxy /etc/nginx/sites-enabled/
ufw allow 11435/tcp
systemctl reload nginx

Generate a secure key: python3 -c "import secrets; print(secrets.token_urlsafe(32))"

Clients then connect to http://your-server:11435 with the header Authorization: Bearer YOUR_SECRET_API_KEY.

Security Notes

• n8n is exposed on port 5678 with Basic Auth -- consider adding a reverse proxy with TLS for production
• Ollama listens on localhost only by default (not exposed to the internet)
• The optional Ollama proxy (port 11435) uses Bearer token authentication
• All credentials are in .env (never committed to git)
• KnowledgeDigest web viewer should be secured with a reverse proxy (e.g., Nginx Basic Auth on port 8788, block direct access to 8787 via firewall)

Part of the ellmos ecosystem

Component	Description
ellmos-ai/rinnsal	Lightweight AI memory & task management
file-bricks/knowledgedigest	Document knowledge base with web UI
file-bricks/NoteSpaceLLM	Desktop document analysis & RAG chat (connects to stack's Ollama)
research-line/research-agent	Academic paper search & analysis

License

MIT