585 lines
18 KiB
Plaintext
585 lines
18 KiB
Plaintext
Automated Development Pipeline - Complete Project Context & Progress Report
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🎯 PROJECT VISION & OBJECTIVES
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Core Vision
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Create a fully automated development pipeline that takes developer requirements in natural language and outputs a complete, production-ready application with minimal human intervention.
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Success Metrics
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80-90% reduction in manual coding for standard applications
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Complete project delivery in under 30 minutes
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Production-ready code quality (80%+ test coverage)
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Zero developer intervention for deployment pipeline
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Support for both monolithic and microservices architectures
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Developer Experience Goal
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Developer opens simple web interface
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Describes what they want in plain English
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Answers a few clarifying questions (if needed)
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Clicks "Generate"
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Gets a live, deployed application with URL
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Can access source code if needed
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🏗️ COMPLETE SYSTEM ARCHITECTURE
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High-Level Flow
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Developer Interface (React)
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↓
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API Gateway (Node.js + JWT)
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↓
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n8n Orchestration Engine
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↓
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┌─────────────┬─────────────┬─────────────┐
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│ AI Services │ Code Services│ Infra Services│
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│- Requirements│- Generator │- Testing │
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│- Tech Stack │- Architecture│- Deployment │
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│- Quality │- Templates │- Monitoring │
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└─────────────┴─────────────┴─────────────┘
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↓
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Data Layer (PostgreSQL + MongoDB + Redis + RabbitMQ)
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↓
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Generated Applications (Local + CloudtopiAA)
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Technology Stack Matrix
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Phase 1 Implementation (Weeks 1-4):
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React + Node.js + PostgreSQL (Full JavaScript)
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React + .NET Core + PostgreSQL (Enterprise)
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Vue.js + Python FastAPI + PostgreSQL (Modern flexible)
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Phase 2 Implementation (Weeks 5-8):
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4. Angular + Java Spring Boot + PostgreSQL (Enterprise Java)
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5. Svelte + Go + PostgreSQL (Performance)
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6. Next.js + Node.js + MongoDB (Modern full-stack)
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Phase 3 Implementation (Weeks 9-12):
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7. React + Python Django + PostgreSQL (Data-heavy)
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8. Vue.js + Ruby Rails + PostgreSQL (Rapid development)
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9. Angular + .NET Core + SQL Server (Microsoft ecosystem)
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📁 PROJECT STRUCTURE
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automated-dev-pipeline/
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├── infrastructure/
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│ ├── docker/ # Docker configurations
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│ ├── terraform/ # Infrastructure as Code
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│ ├── kubernetes/ # K8s manifests
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│ ├── jenkins/ # CI/CD configurations
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│ └── rabbitmq/ # Message queue configs
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├── orchestration/
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│ └── n8n/ # Master workflow engine
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│ ├── workflows/ # n8n workflow definitions
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│ └── custom-nodes/ # Custom n8n nodes
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├── services/
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│ ├── api-gateway/ # Central API gateway (Node.js)
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│ ├── requirement-processor/ # AI requirement analysis (Python)
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│ ├── tech-stack-selector/ # Technology selection AI (Python)
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│ ├── architecture-designer/ # System architecture AI (Python)
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│ ├── code-generator/ # Multi-framework code gen (Python)
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│ ├── test-generator/ # Automated testing (Python)
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│ └── deployment-manager/ # Deployment automation (Python)
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├── frontend/
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│ └── developer-interface/ # React developer UI
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├── databases/
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│ └── scripts/ # DB schemas and migrations
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├── monitoring/
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│ └── configs/ # Prometheus, Grafana configs
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├── generated_projects/ # Output directory
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├── scripts/
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│ └── setup/ # Management scripts
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└── docs/ # Documentation
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🔧 CORE SYSTEM DESIGN DECISIONS
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1. Service Communication Architecture
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Primary Flow: Frontend → API Gateway → n8n → Services
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Direct Communication: Services ↔ Services (performance-critical)
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Async Operations: Services → RabbitMQ → Services
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Real-time Updates: Services → Redis Pub/Sub → Frontend
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2. Error Handling Strategy
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Level 1: Service-Level (3 immediate retries)
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Level 2: n8n Workflow-Level (exponential backoff, 5 attempts)
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Level 3: Dead Letter Queue (manual intervention)
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Level 4: Compensation Transactions (rollback)
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3. State Management
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PostgreSQL: Current state + Event log + Metadata
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Redis: Fast state lookup + Session data + Pub/Sub
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MongoDB: Large objects (generated code, templates)
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State Machine: 15+ project states with audit trail
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4. Security Model
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External: JWT tokens for user authentication
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Internal: mTLS + Service identity tokens
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API Gateway: Rate limiting, input validation, CORS
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Data: Encryption at rest and in transit
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5. Code Storage Strategy
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Generated Projects: Distributed file system (mounted volumes)
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Code Templates: MongoDB (versioned, searchable)
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Metadata: PostgreSQL (relational data)
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Version Control: Gitea/GitLab integration
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📅 COMPLETE IMPLEMENTATION TIMELINE
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PHASE 1: FOUNDATION (WEEKS 1-2) - CURRENT FOCUS
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Week 1: Infrastructure Setup
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✅ COMPLETED: Project directory structure creation
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✅ COMPLETED: Database schemas (PostgreSQL, MongoDB, Redis)
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✅ COMPLETED: Docker infrastructure configuration
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✅ COMPLETED: 6 Python microservices with complete FastAPI code (158 lines each)
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✅ COMPLETED: 1 Node.js API Gateway with complete Express.js code (2,960 bytes)
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✅ COMPLETED: RabbitMQ message queue setup and working
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✅ COMPLETED: Complete startup script suite (7 management scripts)
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✅ COMPLETED: All infrastructure services operational
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Week 2: Core Service Templates & Basic Integration
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🔄 NEXT: Add application services to docker-compose.yml
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⏳ PENDING: Create missing Dockerfiles for Python services
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⏳ PENDING: Create requirements.txt files for Python services
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⏳ PENDING: Service-to-service communication setup
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⏳ PENDING: Basic n8n workflows for service coordination
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⏳ PENDING: Health monitoring and logging implementation
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PHASE 2: AI SERVICES & ORCHESTRATION (WEEKS 3-4)
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Week 3: Requirements Processing & Tech Stack Selection
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⏳ Claude API integration for requirement analysis
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⏳ Natural language processing for requirement validation
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⏳ Technical PRD generation from user input
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⏳ AI-powered technology stack selection algorithm
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⏳ Framework compatibility matrix implementation
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⏳ n8n workflows for AI service coordination
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Week 4: Architecture Design & Planning
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⏳ Monolithic vs microservices decision engine
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⏳ Database schema generation from requirements
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⏳ API contract generation
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⏳ System architecture diagram generation
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⏳ Component relationship mapping
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⏳ Infrastructure requirement calculation
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PHASES 3-6: REMAINING IMPLEMENTATION
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[Detailed timeline for Weeks 5-12 covering Code Generation, Testing, Deployment, and Frontend development]
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📊 CURRENT STATUS & DETAILED PROGRESS
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✅ PHASE 1 FOUNDATION - 85% COMPLETE
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Infrastructure Services: 100% OPERATIONAL
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PostgreSQL:
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Status: ✅ Healthy and connected
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Port: 5432
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Database: dev_pipeline
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User: pipeline_admin
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Connection: Tested and working
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Redis:
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Status: ✅ Healthy and connected (FIXED authentication issue)
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Port: 6379
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Password: redis_secure_2024
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Connection: Tested with authentication
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MongoDB:
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Status: ✅ Healthy and connected
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Port: 27017
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Connection: Tested and working
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RabbitMQ:
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Status: ✅ Healthy with management UI
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AMQP Port: 5672
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Management UI: http://localhost:15672
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Username: pipeline_admin
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Password: rabbit_secure_2024
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Connection: Tested and working
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Application Services: CODE COMPLETE, CONTAINERIZATION PENDING
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API Gateway (Node.js):
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Code: ✅ Complete (2,960 bytes server.js)
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Dependencies: ✅ Complete (package.json with 13 dependencies)
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Dockerfile: ✅ Complete (529 bytes)
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Status: Ready to containerize
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Port: 8000
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Requirement Processor (Python):
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Code: ✅ Complete (158 lines main.py, 4,298 bytes)
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Dependencies: ❌ Missing requirements.txt
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Dockerfile: ❌ Empty (0 bytes)
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Status: Code tested manually, needs containerization
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Port: 8001
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Tech Stack Selector (Python):
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Code: ✅ Complete (158 lines main.py, 4,278 bytes)
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Dependencies: ❌ Missing requirements.txt
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Dockerfile: ❌ Empty (0 bytes)
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Status: Ready for containerization
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Port: 8002
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Architecture Designer (Python):
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Code: ✅ Complete (158 lines main.py, 4,298 bytes)
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Dependencies: ❌ Missing requirements.txt
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Dockerfile: ❌ Empty (0 bytes)
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Status: Ready for containerization
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Port: 8003
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Code Generator (Python):
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Code: ✅ Complete (158 lines main.py, 4,228 bytes)
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Dependencies: ❌ Missing requirements.txt
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Dockerfile: ❌ Empty (0 bytes)
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Status: Ready for containerization
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Port: 8004
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Test Generator (Python):
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Code: ✅ Complete (158 lines main.py, 4,228 bytes)
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Dependencies: ❌ Missing requirements.txt
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Dockerfile: ❌ Empty (0 bytes)
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Status: Ready for containerization
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Port: 8005
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Deployment Manager (Python):
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Code: ✅ Complete (158 lines main.py, 4,268 bytes)
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Dependencies: ❌ Missing requirements.txt
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Dockerfile: ❌ Empty (0 bytes)
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Status: Ready for containerization
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Port: 8006
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Management Scripts: 100% COMPLETE
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Located in scripts/setup/:
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✅ start.sh (7,790 bytes) - Main startup script (FIXED Redis auth)
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✅ stop.sh (1,812 bytes) - Stop all services
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✅ status.sh (4,561 bytes) - Check system status
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✅ validate-phase1.sh (5,455 bytes) - Phase 1 validation
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✅ logs.sh (1,060 bytes) - View service logs
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✅ dev.sh (3,391 bytes) - Development mode
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✅ cleanup.sh (1,701 bytes) - Clean up resources
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Project Configuration Files
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docker-compose.yml:
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Infrastructure services: ✅ Complete
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Application services: ❌ Not added yet
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Networks and volumes: ✅ Complete
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Environment Configuration:
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✅ .env file with all required variables
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✅ Database passwords configured
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✅ Service configurations
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Database Schemas: ✅ Complete PostgreSQL, MongoDB, Redis setup
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🔧 KNOWN ISSUES AND SOLUTIONS
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✅ RESOLVED ISSUES
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Redis Authentication Issue:
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Problem: Startup script couldn't connect to Redis
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Root Cause: Script missing password authentication
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Solution: Fixed startup script to use redis-cli -a redis_secure_2024 ping
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Status: ✅ RESOLVED
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Docker Compose Version Warning:
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Problem: Obsolete version attribute warning
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Status: ⚠️ Cosmetic issue, doesn't affect functionality
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⏳ PENDING ISSUES TO ADDRESS
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Python Service Containerization:
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Issue: Missing requirements.txt and Dockerfiles for 6 Python services
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Impact: Cannot start services with docker-compose
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Solution Needed: Create standardized requirements.txt and Dockerfiles
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Docker Compose Service Definitions:
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Issue: Application services not defined in docker-compose.yml
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Impact: Cannot start full system with single command
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Solution Needed: Add 7 service definitions to docker-compose.yml
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📋 DETAILED NEXT STEPS
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IMMEDIATE ACTIONS (Next 1-2 Hours)
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Step 1: Create Requirements Files
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All Python services use the same dependencies:
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fastapi==0.104.1
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uvicorn==0.24.0
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loguru==0.7.2
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pydantic==2.11.4
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Step 2: Create Dockerfiles
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Standardized Dockerfile template for Python services:
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dockerfileFROM python:3.12-slim
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install -r requirements.txt
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COPY src/ ./src/
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EXPOSE 800X
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CMD ["uvicorn", "src.main:app", "--host", "0.0.0.0", "--port", "800X"]
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Step 3: Add Services to docker-compose.yml
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Add definitions for all 7 application services with proper networking, dependencies, and environment variables.
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Step 4: Test Complete System
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Run ./scripts/setup/start.sh to start all 11 services (4 infrastructure + 7 application).
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Step 5: Run Phase 1 Validation
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Execute ./scripts/setup/validate-phase1.sh to confirm Phase 1 completion.
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PHASE 1 COMPLETION CRITERIA
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✅ All 4 infrastructure services healthy
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⏳ All 7 application services starting successfully
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⏳ API Gateway routing to all microservices
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⏳ Health endpoints responding on all services
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⏳ Service-to-service communication established
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⏳ Phase 1 validation script passing 100%
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🎛️ DETAILED SERVICE SPECIFICATIONS
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Infrastructure Services
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PostgreSQL Database
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Image: postgres:15
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Port: 5432
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Database: dev_pipeline
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User: pipeline_admin
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Password: pipeline_password
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Health: ✅ Confirmed working
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Tables: 8 main tables for project state management
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Redis Cache
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Image: redis:7-alpine
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Port: 6379
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Password: redis_secure_2024
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Persistence: AOF enabled
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Health: ✅ Confirmed working with authentication
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Usage: Caching, sessions, pub/sub
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MongoDB Document Store
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Image: mongo:7
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Port: 27017
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User: pipeline_user
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Password: pipeline_password
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Health: ✅ Confirmed working
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Usage: Code templates, generated projects
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RabbitMQ Message Queue
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Image: Custom (automated-dev-pipeline-rabbitmq)
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AMQP Port: 5672
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Management UI: 15672
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User: pipeline_admin
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Password: rabbit_secure_2024
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Health: ✅ Confirmed working
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Plugins: Management, Prometheus, Federation
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Application Services
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API Gateway (api-gateway)
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Technology: Node.js + Express
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Port: 8000
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Dependencies: 13 packages (express, cors, redis, etc.)
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Features: JWT auth, rate limiting, WebSocket, service discovery
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Code Status: ✅ Complete (2,960 bytes)
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Container Status: ✅ Ready
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Requirement Processor (requirement-processor)
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Technology: Python + FastAPI
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Port: 8001
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Purpose: Natural language processing, PRD generation
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Code Status: ✅ Complete (158 lines, 4,298 bytes)
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Container Status: ⏳ Needs Dockerfile + requirements.txt
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Tech Stack Selector (tech-stack-selector)
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Technology: Python + FastAPI
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Port: 8002
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Purpose: AI-powered technology selection
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Code Status: ✅ Complete (158 lines, 4,278 bytes)
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Container Status: ⏳ Needs Dockerfile + requirements.txt
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Architecture Designer (architecture-designer)
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Technology: Python + FastAPI
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Port: 8003
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Purpose: System architecture design, database schema generation
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Code Status: ✅ Complete (158 lines, 4,298 bytes)
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Container Status: ⏳ Needs Dockerfile + requirements.txt
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Code Generator (code-generator)
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Technology: Python + FastAPI
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Port: 8004
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Purpose: Multi-framework code generation
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Code Status: ✅ Complete (158 lines, 4,228 bytes)
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Container Status: ⏳ Needs Dockerfile + requirements.txt
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Test Generator (test-generator)
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Technology: Python + FastAPI
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Port: 8005
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Purpose: Automated test generation (unit, integration, E2E)
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Code Status: ✅ Complete (158 lines, 4,228 bytes)
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Container Status: ⏳ Needs Dockerfile + requirements.txt
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Deployment Manager (deployment-manager)
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Technology: Python + FastAPI
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Port: 8006
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Purpose: Local and cloud deployment automation
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Code Status: ✅ Complete (158 lines, 4,268 bytes)
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Container Status: ⏳ Needs Dockerfile + requirements.txt
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🗃️ DATABASE ARCHITECTURE
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PostgreSQL Tables (dev_pipeline database)
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projects: Main project entity with status tracking
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tech_stack_decisions: Technology selection results
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system_architectures: Architecture design artifacts
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code_generations: Generated code tracking
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test_results: Test execution results
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deployment_logs: Deployment history
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service_health: Service monitoring
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project_state_transitions: Audit trail
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MongoDB Collections
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code_templates: Framework-specific templates
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framework_configs: Technology configurations
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generated_projects: Complete project storage
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ai_prompts: AI prompt templates
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Redis Data Structures
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Cache Keys: API responses, computed results
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Session Data: User session management
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Pub/Sub Channels: Real-time updates
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Queue Data: Background task processing
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🔗 INTEGRATION POINTS
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Current Integrations
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Docker Network: All services on pipeline_network
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Service Discovery: Via API Gateway routing
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Health Monitoring: All services expose /health endpoints
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Logging: Centralized logging with loguru
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Planned Integrations
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Claude API: Natural language processing, code generation
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CloudtopiAA API: Cloud deployment and infrastructure
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n8n Workflows: Service orchestration
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Git APIs: Repository management (Gitea/GitLab)
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🚨 CRITICAL SUCCESS FACTORS
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Infrastructure Stability: ✅ ACHIEVED - All 4 services operational
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Service Containerization: 🔄 IN PROGRESS - Need to complete Python services
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Inter-service Communication: ⏳ PENDING - Need service mesh setup
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Error Handling: ⏳ PENDING - Need comprehensive error recovery
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Performance: ⏳ PENDING - Need sub-30-minute generation time
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Quality Gates: ⏳ PENDING - Need production-ready code standards
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🎯 PROJECT CONTEXT RESTORATION CHECKLIST
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When resuming this project, verify:
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Environment Check
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✅ Are we in the /Users/yasha/Documents/Tech4biz-Code-Generator/automated-dev-pipeline directory?
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✅ Do all 7 services exist with proper code files?
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✅ Is docker-compose.yml present with infrastructure services?
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✅ Are database scripts in place?
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✅ Can we run ./scripts/setup/start.sh successfully?
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Infrastructure Verification
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✅ PostgreSQL: Accessible on localhost:5432
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✅ Redis: Accessible with password on localhost:6379
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✅ MongoDB: Accessible on localhost:27017
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✅ RabbitMQ: Management UI on http://localhost:15672
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Code Status Verification
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✅ API Gateway: Complete with Dockerfile
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✅ Python Services: All have 158-line main.py files
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❌ Python Services: Missing requirements.txt and Dockerfiles
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❌ docker-compose.yml: Missing application service definitions
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Next Session Action Plan
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Create requirements.txt for all 6 Python services
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Create Dockerfiles for all 6 Python services
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Add service definitions to docker-compose.yml
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Test complete system startup
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Run Phase 1 validation
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Begin Phase 2 planning (n8n + AI integration)
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📍 CURRENT POSITION SUMMARY
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Phase 1 Status: 85% Complete
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Infrastructure: 100% Operational ✅
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Application Code: 100% Complete ✅
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Containerization: 15% Complete (1/7 services) 🔄
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Integration: 0% Complete ⏳
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Immediate Goal: Complete Phase 1 by containerizing all application services
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Next Milestone: Phase 1 validation passing 100% → Begin Phase 2 AI Services Integration
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Time Estimate to Phase 1 Completion: 2-3 hours
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Overall Project Progress: Week 1.8 of 12-week timeline |