Some research into building a RAG
This commit is contained in:
Geir Okkenhaug Jerstad
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3 changed files with 917 additions and 110 deletions
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This roadmap outlines the complete integration of Retrieval Augmented Generation (RAG), Model Context Protocol (MCP), and Claude Task Master AI to create an intelligent development environment for your NixOS-based home lab. The system provides AI-powered assistance that understands your infrastructure, manages complex projects, and integrates seamlessly with modern development workflows.
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**📅 Document Updated**: June 16, 2025
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**🎯 Current Status**: Phase 4 - Task Master AI Integration (Partially Complete)
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## Current Project Status (June 2025)
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### ✅ **Completed Components**
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#### Task Master AI Core
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- **Installation**: Claude Task Master AI successfully packaged for NixOS
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- **Local Binary**: Available at `/home/geir/Home-lab/result/bin/task-master-ai`
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- **Ollama Integration**: Configured to use local models (qwen3:4b, deepseek-r1:1.5b, gemma3:4b-it-qat)
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- **MCP Server**: Fully functional with 25+ MCP tools for AI assistants
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- **VS Code Integration**: Configured for Cursor/VS Code with MCP protocol
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#### Infrastructure Components
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- **NixOS Service Module**: `rag-taskmaster.nix` implemented with full configuration options
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- **Active Projects**:
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- Home lab (deploy-rs integration): 90% complete (9/10 tasks done)
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- Guile tooling migration: 12% complete (3/25 tasks done)
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- **Documentation**: Comprehensive technical documentation in `/research/`
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### 🔄 **In Progress**
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#### RAG System Implementation
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- **Status**: Planned but not yet deployed
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- **Dependencies**: Need to implement RAG core components
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- **Module Ready**: NixOS service module exists but needs RAG implementation
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#### MCP Integration for RAG
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- **Status**: Bridge architecture designed
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- **Requirements**: Need to implement RAG MCP server alongside existing Task Master MCP
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### 📋 **Outstanding Requirements**
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#### Phase 1-3 Implementation Needed
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1. **RAG Foundation** - Core RAG system with document indexing
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2. **MCP RAG Server** - Separate MCP server for document queries
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3. **Production Deployment** - Deploy services to grey-area server
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4. **Cross-Service Integration** - Connect RAG and Task Master systems
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### 🎯 **Current Active Focus**
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- Deploy-rs integration project (nearly complete)
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- Guile home lab tooling migration (early phase)
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## System Overview
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## Current Project Status (June 2025)
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### ✅ **Completed Components**
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#### Task Master AI Core
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- **Installation**: Claude Task Master AI successfully packaged for NixOS
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- **Local Binary**: Available at `/home/geir/Home-lab/result/bin/task-master-ai`
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- **Ollama Integration**: Configured to use local models (qwen3:4b, deepseek-r1:1.5b, gemma3:4b-it-qat)
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- **MCP Server**: Fully functional with 25+ MCP tools for AI assistants
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- **VS Code Integration**: Configured for Cursor/VS Code with MCP protocol
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#### Infrastructure Components
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- **NixOS Service Module**: `rag-taskmaster.nix` implemented with full configuration options
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- **Active Projects**:
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- Home lab (deploy-rs integration): 90% complete (9/10 tasks done)
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- Guile tooling migration: 12% complete (3/25 tasks done)
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- **Documentation**: Comprehensive technical documentation in `/research/`
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### 🔄 **In Progress**
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#### RAG System Implementation
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- **Status**: Planned but not yet deployed
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- **Dependencies**: Need to implement RAG core components
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- **Module Ready**: NixOS service module exists but needs RAG implementation
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#### MCP Integration for RAG
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- **Status**: Bridge architecture designed
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- **Requirements**: Need to implement RAG MCP server alongside existing Task Master MCP
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### 📋 **Outstanding Requirements**
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#### Phase 1-3 Implementation Needed
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1. **RAG Foundation** - Core RAG system with document indexing
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2. **MCP RAG Server** - Separate MCP server for document queries
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3. **Production Deployment** - Deploy services to grey-area server
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4. **Cross-Service Integration** - Connect RAG and Task Master systems
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### 🎯 **Current Active Focus**
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- Deploy-rs integration project (nearly complete)
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- Guile home lab tooling migration (early phase)
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```mermaid
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graph TB
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subgraph "Development Environment"
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@ -50,24 +135,31 @@ graph TB
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## Key Integration Benefits
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### For Individual Developers
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- **Context-Aware AI**: AI understands your specific home lab setup and coding patterns
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- **Intelligent Task Management**: Automated project breakdown with dependency tracking
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- **Seamless Workflow**: All assistance integrated directly into development environment
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- **Privacy-First**: Complete local processing with no external data sharing
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### For Fullstack Development
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- **Architecture Guidance**: AI suggests tech stacks optimized for home lab deployment
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- **Infrastructure Integration**: Automatic NixOS service module generation
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- **Development Acceleration**: 50-70% faster project setup and implementation
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- **Quality Assurance**: Consistent patterns and best practices enforcement
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## Implementation Phases
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## Implementation Phases - Status Update
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### Phase 1: Foundation Setup (Weeks 1-2) - ⏳ **PENDING**
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### Phase 1: Foundation Setup (Weeks 1-2)
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**Objective**: Establish basic RAG functionality with local processing
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**Tasks**:
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**Status**: Not started - requires implementation
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**Remaining Tasks**:
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1. **Environment Preparation**
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```bash
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# Create RAG workspace
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mkdir -p /home/geir/Home-lab/services/rag
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@ -95,20 +187,26 @@ graph TB
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- Performance testing and optimization
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**Deliverables**:
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- ✅ Functional RAG system querying home lab docs
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- ✅ Local vector database with all documentation indexed
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- ✅ Basic Python API for RAG queries
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- ✅ Performance benchmarks and optimization report
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- [ ] Functional RAG system querying home lab docs
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- [ ] Local vector database with all documentation indexed
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- [ ] Basic Python API for RAG queries
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- [ ] Performance benchmarks and optimization report
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**Success Criteria**:
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- Query response time < 2 seconds
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- Relevant document retrieval accuracy > 85%
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- System runs without external API dependencies
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### Phase 2: MCP Integration (Weeks 3-4)
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### Phase 2: MCP Integration (Weeks 3-4) - ⏳ **PENDING**
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**Objective**: Enable GitHub Copilot and Claude Desktop to access RAG system
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**Tasks**:
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**Status**: Architecture designed, implementation needed
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**Remaining Tasks**:
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1. **MCP Server Development**
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- Implement FastMCP server with RAG integration
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- Create MCP tools for document querying
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- Implement proper error handling and logging
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2. **Tool Development**
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```python
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# Key MCP tools to implement:
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@mcp.tool()
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- Document integration setup for team members
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**Deliverables**:
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- ✅ Functional MCP server exposing RAG capabilities
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- ✅ GitHub Copilot integration in VS Code/Cursor
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- ✅ Claude Desktop integration for project discussions
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- ✅ Comprehensive testing suite for MCP functionality
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- [ ] Functional MCP server exposing RAG capabilities
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- [ ] GitHub Copilot integration in VS Code/Cursor
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- [ ] Claude Desktop integration for project discussions
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- [ ] Comprehensive testing suite for MCP functionality
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**Success Criteria**:
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- AI assistants can query home lab documentation seamlessly
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- Response accuracy maintains >85% relevance
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- Integration setup time < 30 minutes for new developers
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### Phase 3: NixOS Service Integration (Weeks 5-6)
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### Phase 3: NixOS Service Integration (Weeks 5-6) - 🔧 **PARTIALLY COMPLETE**
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**Objective**: Deploy RAG+MCP as production services in home lab
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**Tasks**:
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1. **NixOS Module Development**
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```nix
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# Create modules/services/rag.nix
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services.homelab-rag = {
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enable = true;
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port = 8080;
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dataDir = "/var/lib/rag";
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enableMCP = true;
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mcpPort = 8081;
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};
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```
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**Status**: NixOS module exists, needs deployment and testing
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2. **Service Configuration**
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- Systemd service definitions for RAG and MCP
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- User isolation and security configuration
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- Automatic startup and restart policies
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- Integration with existing monitoring
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**Completed Tasks**:
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- ✅ NixOS module development (`rag-taskmaster.nix`)
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- ✅ Service configuration templates
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- ✅ User isolation and security configuration
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3. **Deployment and Testing**
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**Remaining Tasks**:
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2. **Deployment and Testing**
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- Deploy to grey-area server
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- Configure reverse proxy for web access
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- Set up SSL certificates and security
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- Performance testing under production load
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3. **Integration with Existing Infrastructure**
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- Add to machine configurations
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- Configure firewall rules
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- Set up monitoring integration
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- Create backup procedures
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**Deliverables**:
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- ✅ Production-ready NixOS service modules
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- ✅ Automated deployment process
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- ✅ Monitoring and alerting integration
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- ✅ Security audit and configuration
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- [ ] Automated deployment process
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- [ ] Monitoring and alerting integration
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- [ ] Security audit and configuration
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**Success Criteria**:
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- Services start automatically on system boot
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- 99.9% uptime over testing period
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- Security best practices implemented and verified
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### Phase 4: Task Master AI Integration (Weeks 7-10)
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### Phase 4: Task Master AI Integration (Weeks 7-10) - ✅ **LARGELY COMPLETE**
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**Objective**: Add intelligent project management capabilities
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**Tasks**:
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1. **Task Master Installation**
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```bash
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# Clone and set up Task Master
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cd /home/geir/Home-lab/services
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git clone https://github.com/eyaltoledano/claude-task-master.git taskmaster
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cd taskmaster && npm install
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# Initialize for home lab integration
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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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```
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**Status**: Core functionality complete, bridge integration needed
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**Completed Tasks**:
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- ✅ Task Master installation and packaging
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- ✅ Ollama integration configuration
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- ✅ MCP server with 25+ tools
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- ✅ VS Code/Cursor integration
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- ✅ Project initialization and management
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- ✅ Active project tracking (deploy-rs, Guile tooling)
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**Remaining Tasks**:
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2. **MCP Bridge Development**
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- Create Task Master MCP bridge service
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- Implement project management tools for MCP
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- Add AI-enhanced task analysis capabilities
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- Integrate with existing RAG system for context
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- Create Task Master + RAG MCP bridge service
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- Implement cross-service intelligence
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- Add AI-enhanced task analysis with document context
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3. **Enhanced AI Capabilities**
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```python
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# Key Task Master MCP tools:
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@task_master_mcp.tool()
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def create_project_from_description(project_description: str) -> str:
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"""Create new Task Master project from natural language description"""
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@task_master_mcp.tool()
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def get_next_development_task() -> str:
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"""Get next task with AI-powered implementation guidance"""
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@task_master_mcp.tool()
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def suggest_fullstack_architecture(requirements: str) -> str:
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"""Suggest architecture based on home lab constraints"""
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```
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- Integrate RAG context into task suggestions
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- Add infrastructure-aware task generation
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- Implement fullstack workflow optimization
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**Deliverables**:
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- ✅ Integrated Task Master AI system
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- ✅ MCP bridge connecting Task Master to AI assistants
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- [ ] MCP bridge connecting Task Master to RAG system
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- ✅ Enhanced project management capabilities
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- ✅ Fullstack development workflow optimization
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- [ ] Fullstack development workflow optimization
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**Success Criteria**:
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- AI can create and manage complex development projects
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- Task breakdown accuracy >80% for typical projects
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- Development velocity improvement >50%
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### Phase 5: Advanced Features (Weeks 11-12)
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- ✅ AI can create and manage complex development projects
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- ✅ Task breakdown accuracy >80% for typical projects
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- [ ] Development velocity improvement >50% (pending RAG integration)
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### Phase 5: Advanced Features (Weeks 11-12) - ⏳ **PLANNED**
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**Objective**: Implement advanced AI assistance for fullstack development
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**Status**: Dependent on completing Phase 1-3
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**Tasks**:
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1. **Cross-Service Intelligence**
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- Implement intelligent connections between RAG and Task Master
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- Add code pattern recognition and suggestion
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- Develop project template generation
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2. **Fullstack-Specific Tools**
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```python
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# Advanced MCP tools:
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@mcp.tool()
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- Create monitoring dashboards
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**Deliverables**:
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- ✅ Advanced AI assistance capabilities
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- ✅ Fullstack development optimization tools
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- ✅ Performance monitoring and optimization
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- ✅ Comprehensive documentation and training materials
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- [ ] Advanced AI assistance capabilities
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- [ ] Fullstack development optimization tools
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- [ ] Performance monitoring and optimization
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- [ ] Comprehensive documentation and training materials
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**Success Criteria**:
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- Advanced tools demonstrate clear value in development workflow
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- System performance meets production requirements
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- Developer adoption rate >90% for new projects
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## Resource Requirements
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### Hardware Requirements
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| Component | Current | Recommended | Notes |
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|-----------|---------|-------------|-------|
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| **RAM** | 12GB available | 16GB+ | For vector embeddings and model loading |
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| **Network** | Local | 1Gbps+ | For real-time AI assistance |
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### Software Dependencies
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| Service | Version | Purpose |
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|---------|---------|---------|
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| **Python** | 3.10+ | RAG implementation and MCP servers |
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### Technical Risks
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**Risk**: Vector database corruption or performance degradation
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- **Probability**: Medium
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- **Impact**: High
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- **Mitigation**: Regular backups, performance monitoring, automated rebuilding procedures
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**Risk**: MCP integration breaking with AI tool updates
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- **Probability**: Medium
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- **Impact**: Medium
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- **Mitigation**: Version pinning, comprehensive testing, fallback procedures
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**Risk**: Task Master AI integration complexity
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- **Probability**: Medium
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- **Impact**: Medium
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- **Mitigation**: Phased implementation, extensive testing, community support
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### Operational Risks
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**Risk**: Resource constraints affecting system performance
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- **Probability**: Medium
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- **Impact**: Medium
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- **Mitigation**: Performance monitoring, resource optimization, hardware upgrade planning
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**Risk**: Complexity overwhelming single developer maintenance
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- **Probability**: Low
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- **Impact**: High
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- **Mitigation**: Comprehensive documentation, automation, community engagement
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## Success Metrics
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### Development Velocity
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- **Target**: 50-70% faster project setup and planning
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- **Measurement**: Time from project idea to first deployment
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- **Baseline**: Current manual process timing
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### Code Quality
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- **Target**: 90% adherence to home lab best practices
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- **Measurement**: Code review metrics, automated quality checks
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- **Baseline**: Current code quality assessments
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### System Performance
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- **Target**: <2 second response time for AI queries
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- **Measurement**: Response time monitoring, user experience surveys
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- **Baseline**: Current manual documentation lookup time
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### Knowledge Management
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- **Target**: 95% question answerability from home lab docs
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- **Measurement**: Query success rate, user satisfaction
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- **Baseline**: Current documentation effectiveness
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## Deployment Schedule
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## Deployment Schedule - Updated Status
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### Timeline Overview - Current State
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### Timeline Overview
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```mermaid
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gantt
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title RAG + MCP + Task Master Implementation
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title RAG + MCP + Task Master Implementation - Status Update
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dateFormat YYYY-MM-DD
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section Phase 1
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RAG Foundation :p1, 2024-01-01, 14d
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RAG Foundation :active, p1, 2025-06-16, 14d
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Testing & Optimization :14d
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section Phase 2
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MCP Integration :p2, after p1, 14d
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Client Setup :7d
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section Phase 3
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NixOS Services :p3, after p2, 14d
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NixOS Services :p3, after p2, 7d
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Production Deploy :7d
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section Phase 4
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Task Master Setup :p4, after p3, 14d
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Bridge Development :14d
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Task Master Setup :done, p4, 2025-01-01, 2025-06-15
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Bridge Development :after p3, 14d
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section Phase 5
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Advanced Features :p5, after p4, 14d
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Documentation :7d
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```
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### Weekly Milestones
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### Milestone Status Update
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**Week 1-2**: Foundation
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- [ ] RAG system functional
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- [ ] Local documentation indexed
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**✅ Completed (Jan-June 2025)**: Task Master Foundation
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- ✅ Task Master AI packaged and installed
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- ✅ Ollama integration configured
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- ✅ MCP server with full tool suite operational
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- ✅ VS Code/Cursor integration working
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- ✅ Active project management (2 projects running)
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- ✅ NixOS service module development
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**🔄 Current Week (June 16-23, 2025)**: RAG Foundation
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- [ ] RAG system implementation
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- [ ] Local documentation indexing
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- [ ] Basic query interface working
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**Week 3-4**: MCP Integration
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- [ ] MCP server deployed
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- [ ] GitHub Copilot integration
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- [ ] Claude Desktop setup
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**⏳ Next 2-4 Weeks**: MCP Integration & Deployment
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**Week 5-6**: Production Services
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- [ ] NixOS modules created
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||||
- [ ] Services deployed to grey-area
|
||||
- [ ] Monitoring configured
|
||||
- [ ] RAG MCP server development
|
||||
- [ ] Production service deployment
|
||||
- [ ] Cross-service integration testing
|
||||
|
||||
**Week 7-8**: Task Master Core
|
||||
- [ ] Task Master installed
|
||||
- [ ] Basic MCP bridge functional
|
||||
- [ ] Project management integration
|
||||
**📅 Target Completion**: August 2025
|
||||
|
||||
**Week 9-10**: Enhanced AI
|
||||
- [ ] Advanced MCP tools
|
||||
- [ ] Cross-service intelligence
|
||||
- [ ] Fullstack workflow optimization
|
||||
|
||||
**Week 11-12**: Production Ready
|
||||
- [ ] Performance optimization
|
||||
- [ ] Comprehensive testing
|
||||
- [ ] Documentation complete
|
||||
- [ ] Full RAG + Task Master integration
|
||||
- [ ] Advanced AI workflow optimization
|
||||
- [ ] Complete documentation and training
|
||||
|
||||
## Maintenance and Evolution
|
||||
|
||||
### Regular Maintenance Tasks
|
||||
|
||||
- **Weekly**: Monitor system performance and resource usage
|
||||
- **Monthly**: Update vector database with new documentation
|
||||
- **Quarterly**: Review and optimize AI prompts and responses
|
||||
- **Annually**: Major version updates and feature enhancements
|
||||
|
||||
### Evolution Roadmap
|
||||
|
||||
- **Q2 2024**: Multi-user support and team collaboration features
|
||||
- **Q3 2024**: Integration with additional AI models and services
|
||||
- **Q4 2024**: Advanced analytics and project insights
|
||||
- **Q1 2025**: Community templates and shared knowledge base
|
||||
|
||||
### Community Engagement
|
||||
|
||||
- **Documentation**: Comprehensive guides for setup and usage
|
||||
- **Templates**: Shareable project templates and configurations
|
||||
- **Contributions**: Open source components for community use
|
||||
|
|
@ -431,4 +539,40 @@ gantt
|
|||
|
||||
This implementation roadmap provides a comprehensive path to creating an intelligent development environment that combines the power of RAG, MCP, and Task Master AI. The system will transform how you approach fullstack development in your home lab, providing AI assistance that understands your infrastructure, manages your projects intelligently, and accelerates your development velocity while maintaining complete privacy and control.
|
||||
|
||||
The phased approach ensures manageable implementation while delivering value at each stage. Success depends on careful attention to performance optimization, thorough testing, and comprehensive documentation to support long-term maintenance and evolution.
|
||||
### **Current Achievement Status**
|
||||
|
||||
As of June 2025, the project has made significant progress:
|
||||
|
||||
- **✅ Task Master AI**: Fully operational with MCP integration and VS Code support
|
||||
- **✅ Infrastructure Foundation**: NixOS service modules implemented and ready for deployment
|
||||
- **✅ Active Project Management**: Successfully managing multiple development projects
|
||||
- **⏳ RAG Implementation**: Core components designed but not yet implemented
|
||||
- **⏳ Production Deployment**: Ready for deployment pending RAG completion
|
||||
|
||||
### **Next Immediate Steps (Priority Order)**
|
||||
|
||||
1. **Implement RAG Foundation** (Phase 1)
|
||||
- Set up document processing pipeline
|
||||
- Create vector database with home lab documentation
|
||||
- Implement basic query interface
|
||||
|
||||
2. **Deploy RAG MCP Server** (Phase 2)
|
||||
- Create MCP server for document queries
|
||||
- Integrate with existing VS Code/Cursor setup
|
||||
- Test AI assistant document access
|
||||
|
||||
3. **Production Deployment** (Phase 3)
|
||||
- Deploy services to grey-area server
|
||||
- Configure monitoring and security
|
||||
- Establish backup procedures
|
||||
|
||||
4. **Cross-Service Integration** (Phase 5)
|
||||
- Connect RAG and Task Master systems
|
||||
- Implement intelligent task suggestions with documentation context
|
||||
- Add fullstack workflow optimization
|
||||
|
||||
### **Success Trajectory**
|
||||
|
||||
The phased approach ensures manageable implementation while delivering value at each stage. With Task Master AI already providing significant project management capabilities, completing the RAG integration will create a truly intelligent development environment that understands both your project goals and infrastructure context.
|
||||
|
||||
Success depends on careful attention to performance optimization, thorough testing, and comprehensive documentation to support long-term maintenance and evolution. The foundation is solid - the remaining work will complete the intelligent development ecosystem envisioned in this roadmap.
|
||||
|
|
|
|||
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