Netgrimoire/netgrimoire-agent.md

title	description	published	date	tags	editor	dateCreated
Ollama with agent	The smart home reference	true	2026-02-18T22:14:41.533Z		markdown	2026-02-18T22:14:41.533Z

AI Automation Stack - Ollama + n8n + Open WebUI

Overview

This stack provides a complete self-hosted AI automation solution for homelab infrastructure management, documentation generation, and intelligent monitoring. The system consists of four core components that work together to provide AI-powered workflows and knowledge management.

Architecture

┌─────────────────────────────────────────────────┐
│              AI Automation Stack                 │
│                                                  │
│  Open WebUI ────────┐                           │
│  (Chat Interface)   │                           │
│         │           │                           │
│         ▼           ▼                           │
│      Ollama ◄──── Qdrant                        │
│   (LLM Runtime)   (Vector DB)                   │
│         ▲                                       │
│         │                                       │
│       n8n                                       │
│  (Workflow Engine)                              │
│         │                                       │
│         ▼                                       │
│  Forgejo │ Wiki.js │ Monitoring                 │
└─────────────────────────────────────────────────┘

Components

Ollama

• Purpose: Local LLM runtime engine
• Port: 11434
• Resource Usage: 4-6GB RAM (depending on model)
• Recommended Models:
  • qwen2.5-coder:7b - Code analysis and documentation
  • llama3.2:3b - General queries and chat
  • phi3:mini - Lightweight alternative

Open WebUI

• Purpose: User-friendly chat interface with built-in RAG (Retrieval Augmented Generation)
• Port: 3000
• Features:
  • Document ingestion from Wiki.js
  • Conversational interface for querying documentation
  • RAG pipeline for context-aware responses
  • Multi-model support
• Access: http://your-server-ip:3000

Qdrant

• Purpose: Vector database for semantic search and RAG
• Ports: 6333 (HTTP), 6334 (gRPC)
• Resource Usage: ~1GB RAM
• Function: Stores embeddings of your documentation, code, and markdown files

n8n

• Purpose: Workflow automation and orchestration
• Port: 5678
• Default Credentials:
  • Username: admin
  • Password: change-this-password (⚠️ Change this immediately)
• Access: http://your-server-ip:5678

Installation

Prerequisites

• Docker and Docker Compose installed
• 16GB RAM minimum (8GB available for the stack)
• 50GB disk space for models and data

Deployment Steps

1. Create directory structure:

mkdir -p ~/ai-stack/{n8n/workflows}
cd ~/ai-stack

2. Download the compose file:

# Place the ai-stack-compose.yml in this directory
wget [your-internal-url]/ai-stack-compose.yml

3. Configure environment variables:

# Edit the compose file and change:
# - WEBUI_SECRET_KEY
# - N8N_BASIC_AUTH_PASSWORD
# - WEBHOOK_URL (use your server's IP)
# - GENERIC_TIMEZONE
nano ai-stack-compose.yml

4. Start the stack:

docker-compose -f ai-stack-compose.yml up -d

5. Pull Ollama models:

docker exec -it ollama ollama pull qwen2.5-coder:7b
docker exec -it ollama ollama pull llama3.2:3b

6. Verify services:

docker-compose -f ai-stack-compose.yml ps

Configuration

Open WebUI Setup

1. Navigate to http://your-server-ip:3000
2. Create your admin account (first user becomes admin)
3. Go to Settings → Connections and verify Ollama connection
4. Configure Qdrant:
   • Host: qdrant
   • Port: 6333

Setting Up RAG for Wiki.js

1. In Open WebUI, go to Workspace → Knowledge
2. Create a new collection: "Homelab Documentation"
3. Add sources:
   • URL Crawl: Enter your Wiki.js base URL
   • File Upload: Upload markdown files from repositories
4. Process and index the documents

n8n Initial Configuration

1. Navigate to http://your-server-ip:5678
2. Log in with credentials from docker-compose file
3. Import starter workflows from /n8n/workflows/ directory

Use Cases

1. Automated Documentation Generation

Workflow: Forgejo webhook → n8n → Ollama → Wiki.js

When code is pushed to Forgejo:

1. n8n receives webhook from Forgejo
2. Extracts changed files and repo context
3. Sends to Ollama with prompt: "Generate documentation for this code"
4. Posts generated docs to Wiki.js via API

Example n8n Workflow:

Webhook Trigger
  → HTTP Request (Forgejo API - get file contents)
  → Ollama LLM Node (generate docs)
  → HTTP Request (Wiki.js API - create/update page)
  → Send notification (completion)

2. Docker-Compose Standardization

Workflow: Repository scan → compliance check → issue creation

1. n8n runs on schedule (daily/weekly)
2. Queries Forgejo API for all repositories
3. Scans for docker-compose.yml files
4. Compares against template standards stored in Qdrant
5. Generates compliance report with Ollama
6. Creates Forgejo issues for non-compliant repos

3. Intelligent Alert Processing

Workflow: Monitoring alert → AI analysis → smart routing

1. Beszel/Uptime Kuma sends webhook to n8n
2. n8n queries historical data and context
3. Ollama analyzes:
   • Is this expected? (scheduled backup, known maintenance)
   • Severity level
   • Recommended action
4. Routes appropriately:
   • Critical: Immediate notification (Telegram/email)
   • Warning: Log and monitor
   • Info: Suppress (expected behavior)

4. Email Monitoring & Triage

Workflow: IMAP polling → AI classification → action routing

1. n8n polls email inbox every 5 minutes
2. Filters for keywords: "alert", "critical", "down", "failed"
3. Ollama classifies urgency and determines if actionable
4. Routes based on classification:
   • Urgent: Forward to you immediately
   • Informational: Daily digest
   • Spam: Archive

Common Workflows

Example: Repository Documentation Generator

// n8n workflow nodes:

1. Schedule Trigger (daily at 2 AM)
   ↓
2. HTTP Request - Forgejo API
   URL: http://forgejo:3000/api/v1/repos/search
   Method: GET
   ↓
3. Loop Over Items (each repo)
   ↓
4. HTTP Request - Get repo files
   URL: {{$node["Forgejo API"].json["clone_url"]}}/contents
   ↓
5. Filter - Find docker-compose.yml and README.md
   ↓
6. Ollama Node
   Model: qwen2.5-coder:7b
   Prompt: "Analyze this docker-compose file and generate comprehensive 
           documentation including: purpose, services, ports, volumes, 
           environment variables, and setup instructions."
   ↓
7. HTTP Request - Wiki.js API
   URL: http://wikijs:3000/graphql
   Method: POST
   Body: {mutation: createPage(...)}
   ↓
8. Send Notification
   Service: Telegram/Email
   Message: "Documentation updated for {{repo_name}}"

Example: Alert Intelligence Workflow

// n8n workflow nodes:

1. Webhook Trigger
   Path: /webhook/monitoring-alert
   ↓
2. Function Node - Parse Alert Data
   JavaScript: Extract service, metric, value, timestamp
   ↓
3. HTTP Request - Query Historical Data
   URL: http://beszel:8090/api/metrics/history
   ↓
4. Ollama Node
   Model: llama3.2:3b
   Context: Your knowledge base in Qdrant
   Prompt: "Alert: {{alert_message}}
           Historical context: {{historical_data}}
           Is this expected behavior? 
           What's the severity? 
           What action should be taken?"
   ↓
5. Switch Node - Route by Severity
   Conditions:
   - Critical: Route to immediate notification
   - Warning: Route to monitoring channel
   - Info: Route to log only
   ↓
6a. Send Telegram (Critical path)
6b. Post to Slack (Warning path)
6c. Write to Log (Info path)

Maintenance

Model Management

# List installed models
docker exec -it ollama ollama list

# Update a model
docker exec -it ollama ollama pull qwen2.5-coder:7b

# Remove unused models
docker exec -it ollama ollama rm old-model:tag

Backup Important Data

# Backup Qdrant vector database
docker-compose -f ai-stack-compose.yml stop qdrant
tar -czf qdrant-backup-$(date +%Y%m%d).tar.gz ./qdrant_data/
docker-compose -f ai-stack-compose.yml start qdrant

# Backup n8n workflows (automatic to ./n8n/workflows)
tar -czf n8n-backup-$(date +%Y%m%d).tar.gz ./n8n_data/

# Backup Open WebUI data
tar -czf openwebui-backup-$(date +%Y%m%d).tar.gz ./open_webui_data/

Log Monitoring

# View all stack logs
docker-compose -f ai-stack-compose.yml logs -f

# View specific service
docker logs -f ollama
docker logs -f n8n
docker logs -f open-webui

Resource Monitoring

# Check resource usage
docker stats

# Expected usage:
# - ollama: 4-6GB RAM (with model loaded)
# - open-webui: ~500MB RAM
# - qdrant: ~1GB RAM
# - n8n: ~200MB RAM

Troubleshooting

Ollama Not Responding

# Check if Ollama is running
docker logs ollama

# Restart Ollama
docker restart ollama

# Test Ollama API
curl http://localhost:11434/api/tags

Open WebUI Can't Connect to Ollama

1. Check network connectivity:

docker exec -it open-webui ping ollama

2. Verify Ollama URL in Open WebUI settings
3. Restart both containers:

docker restart ollama open-webui

n8n Workflows Failing

1. Check n8n logs:

docker logs n8n

2. Verify webhook URLs are accessible
3. Test Ollama connection from n8n:
   • Create test workflow
   • Add Ollama node
   • Run execution

Qdrant Connection Issues

# Check Qdrant health
curl http://localhost:6333/health

# View Qdrant logs
docker logs qdrant

# Restart if needed
docker restart qdrant

Performance Optimization

Model Selection by Use Case

• Quick queries, chat: llama3.2:3b or phi3:mini (fastest)
• Code analysis: qwen2.5-coder:7b or deepseek-coder:6.7b
• Complex reasoning: mistral:7b or llama3.1:8b

n8n Workflow Optimization

• Use Wait nodes to batch operations
• Enable Execute Once for loops to reduce memory
• Store large data in temporary files instead of node output
• Use Split In Batches for processing large datasets

Qdrant Performance

• Default settings are optimized for homelab use
• Increase collection_shards if indexing >100,000 documents
• Enable quantization for large collections

Security Considerations

Change Default Credentials

# Generate secure password
openssl rand -base64 32

# Update in docker-compose.yml:
# - WEBUI_SECRET_KEY
# - N8N_BASIC_AUTH_PASSWORD

Network Isolation

Consider using a reverse proxy (Traefik, Nginx Proxy Manager) with authentication:

• Limit external access to Open WebUI only
• Keep n8n, Ollama, Qdrant on internal network
• Use VPN for remote access

API Security

• Use strong API tokens for Wiki.js and Forgejo integrations
• Rotate credentials periodically
• Audit n8n workflow permissions

Integration Points

Connecting to Existing Services

Uptime Kuma:

• Configure webhook alerts → n8n webhook URL
• Path: http://your-server-ip:5678/webhook/uptime-kuma

Beszel:

• Use Shoutrrr webhook format
• URL: http://your-server-ip:5678/webhook/beszel

Forgejo:

• Repository webhooks for push events
• URL: http://your-server-ip:5678/webhook/forgejo-push
• Enable in repo settings → Webhooks

Wiki.js:

• GraphQL API endpoint: http://wikijs:3000/graphql
• Create API key in Wiki.js admin panel
• Store in n8n credentials

Advanced Features

Creating Custom n8n Nodes

For frequently used Ollama prompts, create custom nodes:

1. Go to n8n → Settings → Community Nodes
2. Install n8n-nodes-ollama-advanced if available
3. Or create Function nodes with reusable code

Training Custom Models

While Ollama doesn't support fine-tuning directly, you can:

1. Use RAG with your specific documentation
2. Create detailed system prompts in n8n
3. Store organization-specific context in Qdrant

Multi-Agent Workflows

Chain multiple Ollama calls for complex tasks:

Planning Agent → Execution Agent → Review Agent → Output

Example: Code refactoring

1. Planning: Analyze code and create refactoring plan
2. Execution: Generate refactored code
3. Review: Check for errors and improvements
4. Output: Create pull request with changes

Resources

• Ollama Documentation: https://ollama.ai/docs
• Open WebUI Docs: https://docs.openwebui.com
• n8n Documentation: https://docs.n8n.io
• Qdrant Docs: https://qdrant.tech/documentation

Support

For issues or questions:

1. Check container logs first
2. Review this documentation
3. Search n8n community forums
4. Check Ollama Discord/GitHub issues

---

Last Updated: {{current_date}}
Maintained By: Homelab Admin
Status: Production