
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.
439 lines
13 KiB
Nix
439 lines
13 KiB
Nix
# NixOS Ollama Service Configuration
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#
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# This module provides a comprehensive Ollama service configuration for the home lab.
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# Ollama is a tool for running large language models locally with an OpenAI-compatible API.
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#
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# Features:
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# - Secure service isolation with dedicated user
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# - Configurable network binding (localhost by default for security)
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# - Resource management and monitoring
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# - Integration with existing NixOS infrastructure
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# - Optional GPU acceleration support
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# - Comprehensive logging and monitoring
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{
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config,
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lib,
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pkgs,
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...
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}:
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with lib; let
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cfg = config.services.homelab-ollama;
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in {
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options.services.homelab-ollama = {
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enable = mkEnableOption "Ollama local LLM service for home lab";
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package = mkOption {
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type = types.package;
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default = pkgs.ollama;
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description = "The Ollama package to use";
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};
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host = mkOption {
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type = types.str;
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default = "127.0.0.1";
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description = ''
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The host address to bind to. Use "0.0.0.0" to allow external access.
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Default is localhost for security.
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'';
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};
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port = mkOption {
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type = types.port;
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default = 11434;
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description = "The port to bind to";
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};
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dataDir = mkOption {
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type = types.path;
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default = "/var/lib/ollama";
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description = "Directory to store Ollama data including models";
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};
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user = mkOption {
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type = types.str;
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default = "ollama";
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description = "User account under which Ollama runs";
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};
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group = mkOption {
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type = types.str;
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default = "ollama";
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description = "Group under which Ollama runs";
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};
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environmentVariables = mkOption {
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type = types.attrsOf types.str;
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default = {};
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description = ''
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Environment variables for the Ollama service.
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Common variables:
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- OLLAMA_ORIGINS: Allowed origins for CORS (default: http://localhost,http://127.0.0.1)
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- OLLAMA_CONTEXT_LENGTH: Context window size (default: 2048)
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- OLLAMA_NUM_PARALLEL: Number of parallel requests (default: 1)
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- OLLAMA_MAX_QUEUE: Maximum queued requests (default: 512)
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- OLLAMA_DEBUG: Enable debug logging (default: false)
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- OLLAMA_MODELS: Model storage directory
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'';
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example = {
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OLLAMA_ORIGINS = "http://localhost,http://127.0.0.1,http://grey-area.lan";
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OLLAMA_CONTEXT_LENGTH = "4096";
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OLLAMA_DEBUG = "1";
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};
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};
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models = mkOption {
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type = types.listOf types.str;
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default = [];
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description = ''
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List of models to automatically download on service start.
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Models will be pulled using 'ollama pull <model>'.
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Popular models:
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- "llama3.3:8b" - Meta's latest Llama model (8B parameters)
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- "mistral:7b" - Mistral AI's efficient model
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- "codellama:7b" - Code-focused model
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- "gemma2:9b" - Google's Gemma model
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- "qwen2.5:7b" - Multilingual model with good coding
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Note: Models are large (4-32GB each). Ensure adequate storage.
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'';
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example = ["llama3.3:8b" "codellama:7b" "mistral:7b"];
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};
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openFirewall = mkOption {
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type = types.bool;
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default = false;
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description = ''
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Whether to open the firewall for the Ollama service.
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Only enable if you need external access to the API.
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'';
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};
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enableGpuAcceleration = mkOption {
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type = types.bool;
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default = false;
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description = ''
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Enable GPU acceleration for model inference.
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Requires compatible GPU and drivers (NVIDIA CUDA or AMD ROCm).
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For NVIDIA: Ensure nvidia-docker and nvidia-container-toolkit are configured.
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For AMD: Ensure ROCm is installed and configured.
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'';
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};
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resourceLimits = {
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maxMemory = mkOption {
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type = types.nullOr types.str;
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default = null;
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description = ''
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Maximum memory usage for the Ollama service (systemd MemoryMax).
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Use suffixes like "8G", "16G", etc.
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Set to null for no limit.
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'';
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example = "16G";
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};
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maxCpuPercent = mkOption {
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type = types.nullOr types.int;
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default = null;
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description = ''
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Maximum CPU usage percentage (systemd CPUQuota).
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Value between 1-100. Set to null for no limit.
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'';
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example = 80;
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};
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};
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backup = {
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enable = mkOption {
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type = types.bool;
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default = false;
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description = "Enable automatic backup of custom models and configuration";
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};
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destination = mkOption {
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type = types.str;
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default = "/backup/ollama";
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description = "Backup destination directory";
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};
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schedule = mkOption {
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type = types.str;
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default = "daily";
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description = "Backup schedule (systemd timer format)";
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};
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};
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monitoring = {
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enable = mkOption {
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type = types.bool;
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default = true;
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description = "Enable monitoring and health checks";
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};
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healthCheckInterval = mkOption {
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type = types.str;
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default = "30s";
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description = "Health check interval";
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};
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};
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};
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config = mkIf cfg.enable {
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# Ensure the Ollama package is available in the system
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environment.systemPackages = [cfg.package];
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# User and group configuration
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users.users.${cfg.user} = {
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isSystemUser = true;
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group = cfg.group;
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home = cfg.dataDir;
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createHome = true;
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description = "Ollama service user";
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shell = pkgs.bash;
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};
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users.groups.${cfg.group} = {};
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# GPU support configuration
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hardware.opengl = mkIf cfg.enableGpuAcceleration {
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enable = true;
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driSupport = true;
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driSupport32Bit = true;
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};
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# NVIDIA GPU support
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services.xserver.videoDrivers = mkIf (cfg.enableGpuAcceleration && config.hardware.nvidia.modesetting.enable) ["nvidia"];
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# AMD GPU support
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systemd.packages = mkIf (cfg.enableGpuAcceleration && config.hardware.amdgpu.opencl.enable) [pkgs.rocmPackages.clr];
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# Main Ollama service
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systemd.services.ollama = {
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description = "Ollama Local LLM Service";
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wantedBy = ["multi-user.target"];
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after = ["network-online.target"];
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wants = ["network-online.target"];
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environment =
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{
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OLLAMA_HOST = "${cfg.host}:${toString cfg.port}";
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OLLAMA_MODELS = "${cfg.dataDir}/models";
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OLLAMA_RUNNERS_DIR = "${cfg.dataDir}/runners";
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}
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// cfg.environmentVariables;
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serviceConfig = {
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Type = "simple";
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ExecStart = "${cfg.package}/bin/ollama serve";
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User = cfg.user;
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Group = cfg.group;
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Restart = "always";
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RestartSec = "3";
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# Security hardening
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NoNewPrivileges = true;
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ProtectSystem = "strict";
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ProtectHome = true;
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PrivateTmp = true;
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PrivateDevices = mkIf (!cfg.enableGpuAcceleration) true;
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ProtectHostname = true;
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ProtectClock = true;
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ProtectKernelTunables = true;
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ProtectKernelModules = true;
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ProtectKernelLogs = true;
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ProtectControlGroups = true;
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RestrictAddressFamilies = ["AF_UNIX" "AF_INET" "AF_INET6"];
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RestrictNamespaces = true;
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LockPersonality = true;
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RestrictRealtime = true;
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RestrictSUIDSGID = true;
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RemoveIPC = true;
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# Resource limits
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MemoryMax = mkIf (cfg.resourceLimits.maxMemory != null) cfg.resourceLimits.maxMemory;
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CPUQuota = mkIf (cfg.resourceLimits.maxCpuPercent != null) "${toString cfg.resourceLimits.maxCpuPercent}%";
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# File system access
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ReadWritePaths = [cfg.dataDir];
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StateDirectory = "ollama";
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CacheDirectory = "ollama";
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LogsDirectory = "ollama";
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# GPU access for NVIDIA
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SupplementaryGroups = mkIf (cfg.enableGpuAcceleration && config.hardware.nvidia.modesetting.enable) ["video" "render"];
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# For AMD GPU access, allow access to /dev/dri
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DeviceAllow = mkIf (cfg.enableGpuAcceleration && config.hardware.amdgpu.opencl.enable) [
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"/dev/dri"
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"/dev/kfd rw"
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];
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};
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# Ensure data directory exists with correct permissions
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preStart = ''
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mkdir -p ${cfg.dataDir}/{models,runners}
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chown -R ${cfg.user}:${cfg.group} ${cfg.dataDir}
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chmod 755 ${cfg.dataDir}
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'';
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};
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# Model download service (runs after ollama is up)
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systemd.services.ollama-model-download = mkIf (cfg.models != []) {
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description = "Download Ollama Models";
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wantedBy = ["multi-user.target"];
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after = ["ollama.service"];
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wants = ["ollama.service"];
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environment = {
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OLLAMA_HOST = "${cfg.host}:${toString cfg.port}";
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};
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serviceConfig = {
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Type = "oneshot";
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User = cfg.user;
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Group = cfg.group;
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RemainAfterExit = true;
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TimeoutStartSec = "30min"; # Models can be large
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};
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script = ''
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# Wait for Ollama to be ready
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echo "Waiting for Ollama service to be ready..."
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while ! ${cfg.package}/bin/ollama list >/dev/null 2>&1; do
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sleep 2
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done
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echo "Ollama is ready. Downloading configured models..."
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${concatMapStringsSep "\n" (model: ''
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echo "Downloading model: ${model}"
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if ! ${cfg.package}/bin/ollama list | grep -q "^${model}"; then
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${cfg.package}/bin/ollama pull "${model}"
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else
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echo "Model ${model} already exists, skipping download"
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fi
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'')
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cfg.models}
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echo "Model download completed"
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'';
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};
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# Health check service
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systemd.services.ollama-health-check = mkIf cfg.monitoring.enable {
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description = "Ollama Health Check";
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serviceConfig = {
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Type = "oneshot";
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User = cfg.user;
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Group = cfg.group;
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ExecStart = pkgs.writeShellScript "ollama-health-check" ''
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# Basic health check - verify API is responding
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if ! ${pkgs.curl}/bin/curl -f -s "http://${cfg.host}:${toString cfg.port}/api/tags" >/dev/null; then
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echo "Ollama health check failed - API not responding"
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exit 1
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fi
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# Check if we can list models
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if ! ${cfg.package}/bin/ollama list >/dev/null 2>&1; then
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echo "Ollama health check failed - cannot list models"
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exit 1
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fi
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echo "Ollama health check passed"
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'';
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};
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};
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# Health check timer
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systemd.timers.ollama-health-check = mkIf cfg.monitoring.enable {
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description = "Ollama Health Check Timer";
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wantedBy = ["timers.target"];
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timerConfig = {
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OnBootSec = "5min";
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OnUnitActiveSec = cfg.monitoring.healthCheckInterval;
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Persistent = true;
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};
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};
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# Backup service
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systemd.services.ollama-backup = mkIf cfg.backup.enable {
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description = "Backup Ollama Data";
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serviceConfig = {
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Type = "oneshot";
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User = "root"; # Need root for backup operations
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ExecStart = pkgs.writeShellScript "ollama-backup" ''
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mkdir -p "${cfg.backup.destination}"
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# Backup custom models and configuration (excluding large standard models)
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echo "Starting Ollama backup to ${cfg.backup.destination}"
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# Create timestamped backup
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backup_dir="${cfg.backup.destination}/$(date +%Y%m%d_%H%M%S)"
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mkdir -p "$backup_dir"
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# Backup configuration and custom content
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if [ -d "${cfg.dataDir}" ]; then
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# Only backup manifests and small configuration files, not the large model blobs
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find "${cfg.dataDir}" -name "*.json" -o -name "*.yaml" -o -name "*.txt" | \
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${pkgs.rsync}/bin/rsync -av --files-from=- / "$backup_dir/"
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fi
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# Keep only last 7 backups
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find "${cfg.backup.destination}" -maxdepth 1 -type d -name "????????_??????" | \
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sort -r | tail -n +8 | xargs -r rm -rf
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echo "Ollama backup completed"
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'';
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};
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};
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# Backup timer
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systemd.timers.ollama-backup = mkIf cfg.backup.enable {
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description = "Ollama Backup Timer";
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wantedBy = ["timers.target"];
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timerConfig = {
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OnCalendar = cfg.backup.schedule;
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Persistent = true;
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};
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};
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# Firewall configuration
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networking.firewall = mkIf cfg.openFirewall {
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allowedTCPPorts = [cfg.port];
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};
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# Log rotation
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services.logrotate.settings.ollama = {
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files = ["/var/log/ollama/*.log"];
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frequency = "daily";
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rotate = 7;
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compress = true;
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delaycompress = true;
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missingok = true;
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notifempty = true;
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create = "644 ${cfg.user} ${cfg.group}";
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};
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# Add helpful aliases
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environment.shellAliases = {
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ollama-status = "systemctl status ollama";
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ollama-logs = "journalctl -u ollama -f";
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ollama-models = "${cfg.package}/bin/ollama list";
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ollama-pull = "${cfg.package}/bin/ollama pull";
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ollama-run = "${cfg.package}/bin/ollama run";
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};
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# Ensure proper permissions for model directory
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systemd.tmpfiles.rules = [
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"d ${cfg.dataDir} 0755 ${cfg.user} ${cfg.group} -"
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"d ${cfg.dataDir}/models 0755 ${cfg.user} ${cfg.group} -"
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"d ${cfg.dataDir}/runners 0755 ${cfg.user} ${cfg.group} -"
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];
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};
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meta = {
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maintainers = ["Geir Okkenhaug Jerstad"];
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description = "NixOS module for Ollama local LLM service";
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doc = ./ollama.md;
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};
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}
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