
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.
461 lines
13 KiB
Nix
461 lines
13 KiB
Nix
{
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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-rag-taskmaster;
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# Python environment with all RAG and MCP dependencies
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ragPython = pkgs.python3.withPackages (ps:
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with ps; [
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# Core RAG dependencies
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langchain
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langchain-community
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langchain-chroma
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chromadb
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sentence-transformers
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# MCP dependencies
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fastapi
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uvicorn
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pydantic
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aiohttp
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# Additional utilities
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unstructured
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markdown
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requests
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numpy
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# Custom MCP package (would need to be built)
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# (ps.buildPythonPackage rec {
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# pname = "mcp";
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# version = "1.0.0";
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# src = ps.fetchPypi {
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# inherit pname version;
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# sha256 = "0000000000000000000000000000000000000000000000000000";
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# };
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# propagatedBuildInputs = with ps; [ pydantic aiohttp ];
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# })
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]);
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# Node.js environment for Task Master
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nodeEnv = pkgs.nodejs_20;
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# Service configuration files
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ragConfigFile = pkgs.writeText "rag-config.json" (builtins.toJSON {
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ollama_base_url = "http://localhost:11434";
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vector_store_path = "${cfg.dataDir}/chroma_db";
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docs_path = cfg.docsPath;
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chunk_size = cfg.chunkSize;
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chunk_overlap = cfg.chunkOverlap;
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max_retrieval_docs = cfg.maxRetrievalDocs;
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});
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taskMasterConfigFile = pkgs.writeText "taskmaster-config.json" (builtins.toJSON {
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taskmaster_path = "${cfg.dataDir}/taskmaster";
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ollama_base_url = "http://localhost:11434";
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default_model = "llama3.3:8b";
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project_templates = cfg.projectTemplates;
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});
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in {
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options.services.homelab-rag-taskmaster = {
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enable = mkEnableOption "Home Lab RAG + Task Master AI Integration";
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# Basic configuration
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dataDir = mkOption {
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type = types.path;
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default = "/var/lib/rag-taskmaster";
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description = "Directory for RAG and Task Master data";
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};
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docsPath = mkOption {
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type = types.path;
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default = "/home/geir/Home-lab";
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description = "Path to documentation to index";
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};
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# Port configuration
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ragPort = mkOption {
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type = types.port;
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default = 8080;
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description = "Port for RAG API service";
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};
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mcpRagPort = mkOption {
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type = types.port;
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default = 8081;
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description = "Port for RAG MCP server";
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};
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mcpTaskMasterPort = mkOption {
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type = types.port;
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default = 8082;
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description = "Port for Task Master MCP bridge";
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};
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# RAG configuration
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chunkSize = mkOption {
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type = types.int;
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default = 1000;
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description = "Size of document chunks for embedding";
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};
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chunkOverlap = mkOption {
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type = types.int;
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default = 200;
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description = "Overlap between document chunks";
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};
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maxRetrievalDocs = mkOption {
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type = types.int;
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default = 5;
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description = "Maximum number of documents to retrieve for RAG";
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};
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embeddingModel = mkOption {
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type = types.str;
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default = "all-MiniLM-L6-v2";
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description = "Sentence transformer model for embeddings";
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};
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# Task Master configuration
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enableTaskMaster = mkOption {
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type = types.bool;
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default = true;
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description = "Enable Task Master AI integration";
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};
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projectTemplates = mkOption {
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type = types.listOf types.str;
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default = [
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"fullstack-web-app"
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"nixos-service"
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"home-lab-tool"
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"api-service"
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"frontend-app"
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];
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description = "Available project templates for Task Master";
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};
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# Update configuration
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updateInterval = mkOption {
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type = types.str;
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default = "1h";
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description = "How often to update the document index";
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};
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autoUpdateDocs = mkOption {
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type = types.bool;
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default = true;
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description = "Automatically update document index when files change";
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};
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# Security configuration
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enableAuth = mkOption {
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type = types.bool;
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default = false;
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description = "Enable authentication for API access";
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};
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allowedUsers = mkOption {
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type = types.listOf types.str;
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default = ["geir"];
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description = "Users allowed to access the services";
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};
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# Monitoring configuration
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enableMetrics = mkOption {
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type = types.bool;
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default = true;
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description = "Enable Prometheus metrics collection";
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};
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metricsPort = mkOption {
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type = types.port;
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default = 9090;
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description = "Port for Prometheus metrics";
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};
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};
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config = mkIf cfg.enable {
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# Ensure required system packages
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environment.systemPackages = with pkgs; [
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nodeEnv
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ragPython
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git
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];
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# Create system user and group
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users.users.rag-taskmaster = {
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isSystemUser = true;
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group = "rag-taskmaster";
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home = cfg.dataDir;
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createHome = true;
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description = "RAG + Task Master AI service user";
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};
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users.groups.rag-taskmaster = {};
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# Ensure data directories exist
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systemd.tmpfiles.rules = [
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"d ${cfg.dataDir} 0755 rag-taskmaster rag-taskmaster -"
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"d ${cfg.dataDir}/chroma_db 0755 rag-taskmaster rag-taskmaster -"
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"d ${cfg.dataDir}/taskmaster 0755 rag-taskmaster rag-taskmaster -"
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"d ${cfg.dataDir}/logs 0755 rag-taskmaster rag-taskmaster -"
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"d ${cfg.dataDir}/cache 0755 rag-taskmaster rag-taskmaster -"
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];
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# Core RAG service
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systemd.services.homelab-rag = {
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description = "Home Lab RAG Service";
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wantedBy = ["multi-user.target"];
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after = ["network.target" "ollama.service"];
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wants = ["ollama.service"];
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serviceConfig = {
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Type = "simple";
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User = "rag-taskmaster";
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Group = "rag-taskmaster";
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WorkingDirectory = cfg.dataDir;
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ExecStart = "${ragPython}/bin/python -m rag_service --config ${ragConfigFile}";
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ExecReload = "${pkgs.coreutils}/bin/kill -HUP $MAINPID";
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Restart = "always";
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RestartSec = 10;
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# Security settings
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NoNewPrivileges = true;
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PrivateTmp = true;
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ProtectSystem = "strict";
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ProtectHome = true;
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ReadWritePaths = [cfg.dataDir];
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ReadOnlyPaths = [cfg.docsPath];
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# Resource limits
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MemoryMax = "4G";
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CPUQuota = "200%";
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};
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environment = {
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PYTHONPATH = "${ragPython}/${ragPython.sitePackages}";
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OLLAMA_BASE_URL = "http://localhost:11434";
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VECTOR_STORE_PATH = "${cfg.dataDir}/chroma_db";
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DOCS_PATH = cfg.docsPath;
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LOG_LEVEL = "INFO";
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};
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};
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# RAG MCP Server
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systemd.services.homelab-rag-mcp = {
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description = "Home Lab RAG MCP Server";
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wantedBy = ["multi-user.target"];
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after = ["network.target" "homelab-rag.service"];
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wants = ["homelab-rag.service"];
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serviceConfig = {
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Type = "simple";
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User = "rag-taskmaster";
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Group = "rag-taskmaster";
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WorkingDirectory = cfg.dataDir;
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ExecStart = "${ragPython}/bin/python -m mcp_rag_server --config ${ragConfigFile}";
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Restart = "always";
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RestartSec = 10;
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# Security settings
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NoNewPrivileges = true;
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PrivateTmp = true;
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ProtectSystem = "strict";
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ProtectHome = true;
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ReadWritePaths = [cfg.dataDir];
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ReadOnlyPaths = [cfg.docsPath];
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};
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environment = {
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PYTHONPATH = "${ragPython}/${ragPython.sitePackages}";
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OLLAMA_BASE_URL = "http://localhost:11434";
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VECTOR_STORE_PATH = "${cfg.dataDir}/chroma_db";
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DOCS_PATH = cfg.docsPath;
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MCP_PORT = toString cfg.mcpRagPort;
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};
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};
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# Task Master setup service (runs once to initialize)
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systemd.services.homelab-taskmaster-setup = mkIf cfg.enableTaskMaster {
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description = "Task Master AI Setup";
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after = ["network.target"];
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wantedBy = ["multi-user.target"];
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serviceConfig = {
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Type = "oneshot";
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User = "rag-taskmaster";
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Group = "rag-taskmaster";
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WorkingDirectory = "${cfg.dataDir}/taskmaster";
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RemainAfterExit = true;
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};
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environment = {
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NODE_ENV = "production";
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PATH = "${nodeEnv}/bin:${pkgs.git}/bin";
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};
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script = ''
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# Clone Task Master if not exists
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if [ ! -d "${cfg.dataDir}/taskmaster/.git" ]; then
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${pkgs.git}/bin/git clone https://github.com/eyaltoledano/claude-task-master.git ${cfg.dataDir}/taskmaster
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cd ${cfg.dataDir}/taskmaster
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${nodeEnv}/bin/npm install
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# Initialize with home lab configuration
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${nodeEnv}/bin/npx task-master init --yes \
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--name "Home Lab Development" \
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--description "NixOS-based home lab and fullstack development projects" \
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--author "Geir" \
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--version "1.0.0"
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fi
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# Ensure proper permissions
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chown -R rag-taskmaster:rag-taskmaster ${cfg.dataDir}/taskmaster
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'';
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};
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# Task Master MCP Bridge
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systemd.services.homelab-taskmaster-mcp = mkIf cfg.enableTaskMaster {
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description = "Task Master MCP Bridge";
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wantedBy = ["multi-user.target"];
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after = ["network.target" "homelab-taskmaster-setup.service" "homelab-rag.service"];
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wants = ["homelab-taskmaster-setup.service" "homelab-rag.service"];
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serviceConfig = {
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Type = "simple";
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User = "rag-taskmaster";
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Group = "rag-taskmaster";
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WorkingDirectory = "${cfg.dataDir}/taskmaster";
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ExecStart = "${ragPython}/bin/python -m mcp_taskmaster_bridge --config ${taskMasterConfigFile}";
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Restart = "always";
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RestartSec = 10;
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# Security settings
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NoNewPrivileges = true;
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PrivateTmp = true;
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ProtectSystem = "strict";
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ProtectHome = true;
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ReadWritePaths = [cfg.dataDir];
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ReadOnlyPaths = [cfg.docsPath];
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};
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environment = {
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PYTHONPATH = "${ragPython}/${ragPython.sitePackages}";
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NODE_ENV = "production";
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PATH = "${nodeEnv}/bin:${pkgs.git}/bin";
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OLLAMA_BASE_URL = "http://localhost:11434";
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TASKMASTER_PATH = "${cfg.dataDir}/taskmaster";
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MCP_PORT = toString cfg.mcpTaskMasterPort;
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};
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};
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# Document indexing service (periodic update)
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systemd.services.homelab-rag-indexer = mkIf cfg.autoUpdateDocs {
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description = "Home Lab RAG Document Indexer";
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serviceConfig = {
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Type = "oneshot";
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User = "rag-taskmaster";
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Group = "rag-taskmaster";
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WorkingDirectory = cfg.dataDir;
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ExecStart = "${ragPython}/bin/python -m rag_indexer --config ${ragConfigFile} --update";
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};
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environment = {
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PYTHONPATH = "${ragPython}/${ragPython.sitePackages}";
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DOCS_PATH = cfg.docsPath;
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VECTOR_STORE_PATH = "${cfg.dataDir}/chroma_db";
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};
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};
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# Timer for periodic document updates
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systemd.timers.homelab-rag-indexer = mkIf cfg.autoUpdateDocs {
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description = "Periodic RAG document indexing";
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wantedBy = ["timers.target"];
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timerConfig = {
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OnBootSec = "5m";
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OnUnitActiveSec = cfg.updateInterval;
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Unit = "homelab-rag-indexer.service";
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};
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};
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# Prometheus metrics exporter (if enabled)
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systemd.services.homelab-rag-metrics = mkIf cfg.enableMetrics {
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description = "RAG + Task Master Metrics Exporter";
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wantedBy = ["multi-user.target"];
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after = ["network.target"];
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serviceConfig = {
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Type = "simple";
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User = "rag-taskmaster";
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Group = "rag-taskmaster";
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WorkingDirectory = cfg.dataDir;
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ExecStart = "${ragPython}/bin/python -m metrics_exporter --port ${toString cfg.metricsPort}";
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Restart = "always";
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RestartSec = 10;
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};
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environment = {
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PYTHONPATH = "${ragPython}/${ragPython.sitePackages}";
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METRICS_PORT = toString cfg.metricsPort;
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RAG_SERVICE_URL = "http://localhost:${toString cfg.ragPort}";
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};
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};
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# Firewall configuration
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networking.firewall.allowedTCPPorts =
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mkIf (!cfg.enableAuth) [
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cfg.ragPort
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cfg.mcpRagPort
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cfg.mcpTaskMasterPort
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]
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++ optionals cfg.enableMetrics [cfg.metricsPort];
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# Nginx reverse proxy configuration (optional)
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services.nginx.virtualHosts."rag.home.lab" = mkIf config.services.nginx.enable {
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listen = [
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{
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addr = "0.0.0.0";
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port = 80;
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}
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{
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addr = "0.0.0.0";
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port = 443;
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ssl = true;
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}
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];
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locations = {
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"/api/rag/" = {
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proxyPass = "http://localhost:${toString cfg.ragPort}/";
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proxyWebsockets = true;
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};
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"/api/mcp/rag/" = {
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proxyPass = "http://localhost:${toString cfg.mcpRagPort}/";
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proxyWebsockets = true;
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};
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"/api/mcp/taskmaster/" = mkIf cfg.enableTaskMaster {
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proxyPass = "http://localhost:${toString cfg.mcpTaskMasterPort}/";
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proxyWebsockets = true;
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};
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"/metrics" = mkIf cfg.enableMetrics {
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proxyPass = "http://localhost:${toString cfg.metricsPort}/";
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};
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};
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# SSL configuration would go here if needed
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# sslCertificate = "/path/to/cert";
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# sslCertificateKey = "/path/to/key";
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};
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};
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}
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