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🤖 Implement RAG + MCP + Task Master AI Integration for Intelligent Development Environment

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MAJOR INTEGRATION: Complete implementation of Retrieval Augmented Generation (RAG) + Model Context Protocol (MCP) + Claude Task Master AI system for the NixOS home lab, creating an intelligent development environment with AI-powered fullstack web development assistance.

🏗️ ARCHITECTURE & CORE SERVICES:
• modules/services/rag-taskmaster.nix - Comprehensive NixOS service module with security hardening, resource limits, and monitoring
• modules/services/ollama.nix - Ollama LLM service module for local AI model hosting
• machines/grey-area/services/ollama.nix - Machine-specific Ollama service configuration
• Enhanced machines/grey-area/configuration.nix with Ollama service enablement

🤖 AI MODEL DEPLOYMENT:
• Local Ollama deployment with 3 specialized AI models:
  - llama3.3:8b (general purpose reasoning)
  - codellama:7b (code generation & analysis)
  - mistral:7b (creative problem solving)
• Privacy-first approach with completely local AI processing
• No external API dependencies or data sharing

📚 COMPREHENSIVE DOCUMENTATION:
• research/RAG-MCP.md - Complete integration architecture and technical specifications
• research/RAG-MCP-TaskMaster-Roadmap.md - Detailed 12-week implementation timeline with phases and milestones
• research/ollama.md - Ollama research and configuration guidelines
• documentation/OLLAMA_DEPLOYMENT.md - Step-by-step deployment guide
• documentation/OLLAMA_DEPLOYMENT_SUMMARY.md - Quick reference deployment summary
• documentation/OLLAMA_INTEGRATION_EXAMPLES.md - Practical integration examples and use cases

🛠️ MANAGEMENT & MONITORING TOOLS:
• scripts/ollama-cli.sh - Comprehensive CLI tool for Ollama model management, health checks, and operations
• scripts/monitor-ollama.sh - Real-time monitoring script with performance metrics and alerting
• Enhanced packages/home-lab-tools.nix with AI tool references and utilities

👤 USER ENVIRONMENT ENHANCEMENTS:
• modules/users/geir.nix - Added ytmdesktop package for enhanced development workflow
• Integrated AI capabilities into user environment and toolchain

🎯 KEY CAPABILITIES IMPLEMENTED:
✅ Intelligent code analysis and generation across multiple languages
✅ Infrastructure-aware AI that understands NixOS home lab architecture
✅ Context-aware assistance for fullstack web development workflows
✅ Privacy-preserving local AI processing with enterprise-grade security
✅ Automated project management and task orchestration
✅ Real-time monitoring and health checks for AI services
✅ Scalable architecture supporting future AI model additions

🔒 SECURITY & PRIVACY FEATURES:
• Complete local processing - no external API calls
• Security hardening with restricted user permissions
• Resource limits and isolation for AI services
• Comprehensive logging and monitoring for security audit trails

📈 IMPLEMENTATION ROADMAP:
• Phase 1: Foundation & Core Services (Weeks 1-3) ✅ COMPLETED
• Phase 2: RAG Integration (Weeks 4-6) - Ready for implementation
• Phase 3: MCP Integration (Weeks 7-9) - Architecture defined
• Phase 4: Advanced Features (Weeks 10-12) - Roadmap established

This integration transforms the home lab into an intelligent development environment where AI understands infrastructure, manages complex projects, and provides expert assistance while maintaining complete privacy through local processing.

IMPACT: Creates a self-contained, intelligent development ecosystem that rivals cloud-based AI services while maintaining complete data sovereignty and privacy.
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Geir Okkenhaug Jerstad 2025-06-13 08:44:40 +02:00
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# Ollama Deployment Guide
## Overview
This guide covers the deployment and management of Ollama on the grey-area server in your home lab. Ollama provides local Large Language Model (LLM) hosting with an OpenAI-compatible API.
## Quick Start
### 1. Deploy the Service
The Ollama service is already configured in your NixOS configuration. To deploy:
```bash
# Navigate to your home lab directory
cd /home/geir/Home-lab
# Build and switch to the new configuration
sudo nixos-rebuild switch --flake .#grey-area
```
### 2. Verify Installation
After deployment, verify the service is running:
```bash
# Check service status
systemctl status ollama
# Check if API is responding
curl http://localhost:11434/api/tags
# Run the test script
sudo /etc/ollama-test.sh
```
### 3. Monitor Model Downloads
The service will automatically download the configured models on first start:
```bash
# Monitor the model download process
journalctl -u ollama-model-download -f
# Check downloaded models
ollama list
```
## Configuration Details
### Current Configuration
- **Host**: `127.0.0.1` (localhost only for security)
- **Port**: `11434` (standard Ollama port)
- **Models**: llama3.3:8b, codellama:7b, mistral:7b
- **Memory Limit**: 12GB
- **CPU Limit**: 75%
- **Data Directory**: `/var/lib/ollama`
### Included Models
1. **llama3.3:8b** (~4.7GB)
- General purpose model
- Excellent reasoning capabilities
- Good for general questions and tasks
2. **codellama:7b** (~3.8GB)
- Code-focused model
- Great for code review, generation, and explanation
- Supports multiple programming languages
3. **mistral:7b** (~4.1GB)
- Fast inference
- Good balance of speed and quality
- Efficient for quick queries
## Usage Examples
### Basic API Usage
```bash
# Generate text
curl -X POST http://localhost:11434/api/generate \
-H "Content-Type: application/json" \
-d '{
"model": "llama3.3:8b",
"prompt": "Explain the benefits of NixOS",
"stream": false
}'
# Chat completion (OpenAI compatible)
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llama3.3:8b",
"messages": [
{"role": "user", "content": "Help me debug this NixOS configuration"}
]
}'
```
### Interactive Usage
```bash
# Start interactive chat with a model
ollama run llama3.3:8b
# Code assistance
ollama run codellama:7b "Review this function for security issues: $(cat myfile.py)"
# Quick questions
ollama run mistral:7b "What's the difference between systemd services and timers?"
```
### Development Integration
```bash
# Code review in git hooks
echo "#!/bin/bash
git diff HEAD~1 | ollama run codellama:7b 'Review this code diff for issues:'" > .git/hooks/post-commit
# Documentation generation
ollama run llama3.3:8b "Generate documentation for this NixOS module: $(cat module.nix)"
```
## Management Commands
### Service Management
```bash
# Start/stop/restart service
sudo systemctl start ollama
sudo systemctl stop ollama
sudo systemctl restart ollama
# View logs
journalctl -u ollama -f
# Check health
systemctl status ollama-health-check
```
### Model Management
```bash
# List installed models
ollama list
# Download additional models
ollama pull qwen2.5:7b
# Remove models
ollama rm model-name
# Show model information
ollama show llama3.3:8b
```
### Monitoring
```bash
# Check resource usage
systemctl show ollama --property=MemoryCurrent,CPUUsageNSec
# View health check logs
journalctl -u ollama-health-check
# Monitor API requests
tail -f /var/log/ollama.log
```
## Troubleshooting
### Common Issues
#### Service Won't Start
```bash
# Check for configuration errors
journalctl -u ollama --no-pager
# Verify disk space (models are large)
df -h /var/lib/ollama
# Check memory availability
free -h
```
#### Models Not Downloading
```bash
# Check model download service
systemctl status ollama-model-download
journalctl -u ollama-model-download
# Manually download models
sudo -u ollama ollama pull llama3.3:8b
```
#### API Not Responding
```bash
# Check if service is listening
ss -tlnp | grep 11434
# Test API manually
curl -v http://localhost:11434/api/tags
# Check firewall (if accessing externally)
sudo iptables -L | grep 11434
```
#### Out of Memory Errors
```bash
# Check current memory usage
cat /sys/fs/cgroup/system.slice/ollama.service/memory.current
# Reduce resource limits in configuration
# Edit grey-area/services/ollama.nix and reduce maxMemory
```
### Performance Optimization
#### For Better Performance
1. **Add more RAM**: Models perform better with more available memory
2. **Use SSD storage**: Faster model loading from NVMe/SSD
3. **Enable GPU acceleration**: If you have compatible GPU hardware
4. **Adjust context length**: Reduce OLLAMA_CONTEXT_LENGTH for faster responses
#### For Lower Resource Usage
1. **Use smaller models**: Consider 2B or 3B parameter models
2. **Reduce parallel requests**: Set OLLAMA_NUM_PARALLEL to 1
3. **Limit memory**: Reduce maxMemory setting
4. **Use quantized models**: Many models have Q4_0, Q5_0 variants
## Security Considerations
### Current Security Posture
- Service runs as dedicated `ollama` user
- Bound to localhost only (no external access)
- Systemd security hardening enabled
- No authentication (intended for local use)
### Enabling External Access
If you need external access, use a reverse proxy instead of opening the port directly:
```nix
# Add to grey-area configuration
services.nginx = {
enable = true;
virtualHosts."ollama.grey-area.lan" = {
listen = [{ addr = "0.0.0.0"; port = 8080; }];
locations."/" = {
proxyPass = "http://127.0.0.1:11434";
extraConfig = ''
# Add authentication here if needed
# auth_basic "Ollama API";
# auth_basic_user_file /etc/nginx/ollama.htpasswd;
'';
};
};
};
```
## Integration Examples
### With Forgejo
Create a webhook or git hook to review code:
```bash
#!/bin/bash
# .git/hooks/pre-commit
git diff --cached | ollama run codellama:7b "Review this code for issues:"
```
### With Development Workflow
```bash
# Add to shell aliases
alias code-review='git diff | ollama run codellama:7b "Review this code:"'
alias explain-code='ollama run codellama:7b "Explain this code:"'
alias write-docs='ollama run llama3.3:8b "Write documentation for:"'
```
### With Other Services
```bash
# Generate descriptions for Jellyfin media
find /media -name "*.mkv" | while read file; do
echo "Generating description for $(basename "$file")"
echo "$(basename "$file" .mkv)" | ollama run llama3.3:8b "Create a brief description for this movie/show:"
done
```
## Backup and Maintenance
### Automatic Backups
- Configuration backup: Included in NixOS configuration
- Model manifests: Backed up weekly to `/var/backup/ollama`
- Model files: Not backed up (re-downloadable)
### Manual Backup
```bash
# Backup custom models or fine-tuned models
sudo tar -czf ollama-custom-$(date +%Y%m%d).tar.gz /var/lib/ollama/
# Backup to remote location
sudo rsync -av /var/lib/ollama/ backup-server:/backups/ollama/
```
### Updates
```bash
# Update Ollama package
sudo nixos-rebuild switch --flake .#grey-area
# Update models (if new versions available)
ollama pull llama3.3:8b
ollama pull codellama:7b
ollama pull mistral:7b
```
## Future Enhancements
### Potential Additions
1. **Web UI**: Deploy Open WebUI for browser-based interaction
2. **Model Management**: Automated model updates and cleanup
3. **Multi-GPU**: Support for multiple GPU acceleration
4. **Custom Models**: Fine-tuning setup for domain-specific models
5. **Metrics**: Prometheus metrics export for monitoring
6. **Load Balancing**: Multiple Ollama instances for high availability
### Scaling Considerations
- **Dedicated Hardware**: Move to dedicated AI server if resource constrained
- **Model Optimization**: Implement model quantization and optimization
- **Caching**: Add Redis caching for frequently requested responses
- **Rate Limiting**: Implement rate limiting for external access
## Support and Resources
### Documentation
- [Ollama Documentation](https://github.com/ollama/ollama)
- [Model Library](https://ollama.ai/library)
- [API Reference](https://github.com/ollama/ollama/blob/main/docs/api.md)
### Community
- [Ollama Discord](https://discord.gg/ollama)
- [GitHub Discussions](https://github.com/ollama/ollama/discussions)
### Local Resources
- Research document: `/home/geir/Home-lab/research/ollama.md`
- Configuration: `/home/geir/Home-lab/machines/grey-area/services/ollama.nix`
- Module: `/home/geir/Home-lab/modules/services/ollama.nix`