Automated Development Pipeline - Complete Project Context & Progress Report
🎯 PROJECT VISION & OBJECTIVES
Core Vision
Create a fully automated development pipeline that takes developer requirements in natural language and outputs a complete, production-ready application with minimal human intervention.
Success Metrics

80-90% reduction in manual coding for standard applications
Complete project delivery in under 30 minutes
Production-ready code quality (80%+ test coverage)
Zero developer intervention for deployment pipeline
Support for both monolithic and microservices architectures

Developer Experience Goal

Developer opens simple web interface
Describes what they want in plain English
Answers a few clarifying questions (if needed)
Clicks "Generate"
Gets a live, deployed application with URL
Can access source code if needed


🏗️ COMPLETE SYSTEM ARCHITECTURE
High-Level Flow
Developer Interface (React)
        ↓
API Gateway (Node.js + JWT)
        ↓
n8n Orchestration Engine
        ↓
┌─────────────┬─────────────┬─────────────┐
│ AI Services │ Code Services│ Infra Services│
│- Requirements│- Generator   │- Testing     │
│- Tech Stack │- Architecture│- Deployment  │
│- Quality    │- Templates   │- Monitoring  │
└─────────────┴─────────────┴─────────────┘
        ↓
Data Layer (PostgreSQL + MongoDB + Redis + RabbitMQ)
        ↓
Generated Applications (Local + CloudtopiAA)
Technology Stack Matrix
Phase 1 Implementation (Weeks 1-4):

React + Node.js + PostgreSQL (Full JavaScript)
React + .NET Core + PostgreSQL (Enterprise)
Vue.js + Python FastAPI + PostgreSQL (Modern flexible)

Phase 2 Implementation (Weeks 5-8):
4. Angular + Java Spring Boot + PostgreSQL (Enterprise Java)
5. Svelte + Go + PostgreSQL (Performance)
6. Next.js + Node.js + MongoDB (Modern full-stack)
Phase 3 Implementation (Weeks 9-12):
7. React + Python Django + PostgreSQL (Data-heavy)
8. Vue.js + Ruby Rails + PostgreSQL (Rapid development)
9. Angular + .NET Core + SQL Server (Microsoft ecosystem)

📁 PROJECT STRUCTURE
automated-dev-pipeline/
├── infrastructure/
│   ├── docker/                    # Docker configurations
│   ├── terraform/                 # Infrastructure as Code
│   ├── kubernetes/                # K8s manifests
│   ├── jenkins/                   # CI/CD configurations
│   └── rabbitmq/                  # Message queue configs
├── orchestration/
│   └── n8n/                      # Master workflow engine
│       ├── workflows/             # n8n workflow definitions
│       └── custom-nodes/          # Custom n8n nodes
├── services/
│   ├── api-gateway/              # Central API gateway (Node.js)
│   ├── requirement-processor/     # AI requirement analysis (Python)
│   ├── tech-stack-selector/      # Technology selection AI (Python)
│   ├── architecture-designer/    # System architecture AI (Python)
│   ├── code-generator/           # Multi-framework code gen (Python)
│   ├── test-generator/           # Automated testing (Python)
│   └── deployment-manager/       # Deployment automation (Python)
├── frontend/
│   └── developer-interface/      # React developer UI
├── databases/
│   └── scripts/                  # DB schemas and migrations
├── monitoring/
│   └── configs/                  # Prometheus, Grafana configs
├── generated_projects/           # Output directory
├── scripts/
│   └── setup/                    # Management scripts
└── docs/                         # Documentation

🔧 CORE SYSTEM DESIGN DECISIONS
1. Service Communication Architecture

Primary Flow: Frontend → API Gateway → n8n → Services
Direct Communication: Services ↔ Services (performance-critical)
Async Operations: Services → RabbitMQ → Services
Real-time Updates: Services → Redis Pub/Sub → Frontend

2. Error Handling Strategy

Level 1: Service-Level (3 immediate retries)
Level 2: n8n Workflow-Level (exponential backoff, 5 attempts)
Level 3: Dead Letter Queue (manual intervention)
Level 4: Compensation Transactions (rollback)

3. State Management

PostgreSQL: Current state + Event log + Metadata
Redis: Fast state lookup + Session data + Pub/Sub
MongoDB: Large objects (generated code, templates)
State Machine: 15+ project states with audit trail

4. Security Model

External: JWT tokens for user authentication
Internal: mTLS + Service identity tokens
API Gateway: Rate limiting, input validation, CORS
Data: Encryption at rest and in transit

5. Code Storage Strategy

Generated Projects: Distributed file system (mounted volumes)
Code Templates: MongoDB (versioned, searchable)
Metadata: PostgreSQL (relational data)
Version Control: Gitea/GitLab integration


📅 COMPLETE IMPLEMENTATION TIMELINE
PHASE 1: FOUNDATION (WEEKS 1-2) - CURRENT FOCUS
Week 1: Infrastructure Setup

✅ COMPLETED: Project directory structure creation
✅ COMPLETED: Database schemas (PostgreSQL, MongoDB, Redis)
✅ COMPLETED: Docker infrastructure configuration
✅ COMPLETED: 6 Python microservices with complete FastAPI code (158 lines each)
✅ COMPLETED: 1 Node.js API Gateway with complete Express.js code (2,960 bytes)
✅ COMPLETED: RabbitMQ message queue setup and working
✅ COMPLETED: Complete startup script suite (7 management scripts)
✅ COMPLETED: All infrastructure services operational

Week 2: Core Service Templates & Basic Integration

🔄 NEXT: Add application services to docker-compose.yml
⏳ PENDING: Create missing Dockerfiles for Python services
⏳ PENDING: Create requirements.txt files for Python services
⏳ PENDING: Service-to-service communication setup
⏳ PENDING: Basic n8n workflows for service coordination
⏳ PENDING: Health monitoring and logging implementation

PHASE 2: AI SERVICES & ORCHESTRATION (WEEKS 3-4)
Week 3: Requirements Processing & Tech Stack Selection

⏳ Claude API integration for requirement analysis
⏳ Natural language processing for requirement validation
⏳ Technical PRD generation from user input
⏳ AI-powered technology stack selection algorithm
⏳ Framework compatibility matrix implementation
⏳ n8n workflows for AI service coordination

Week 4: Architecture Design & Planning

⏳ Monolithic vs microservices decision engine
⏳ Database schema generation from requirements
⏳ API contract generation
⏳ System architecture diagram generation
⏳ Component relationship mapping
⏳ Infrastructure requirement calculation

PHASES 3-6: REMAINING IMPLEMENTATION
[Detailed timeline for Weeks 5-12 covering Code Generation, Testing, Deployment, and Frontend development]

📊 CURRENT STATUS & DETAILED PROGRESS
✅ PHASE 1 FOUNDATION - 85% COMPLETE
Infrastructure Services: 100% OPERATIONAL

PostgreSQL:

Status: ✅ Healthy and connected
Port: 5432
Database: dev_pipeline
User: pipeline_admin
Connection: Tested and working


Redis:

Status: ✅ Healthy and connected (FIXED authentication issue)
Port: 6379
Password: redis_secure_2024
Connection: Tested with authentication


MongoDB:

Status: ✅ Healthy and connected
Port: 27017
Connection: Tested and working


RabbitMQ:

Status: ✅ Healthy with management UI
AMQP Port: 5672
Management UI: http://localhost:15672
Username: pipeline_admin
Password: rabbit_secure_2024
Connection: Tested and working



Application Services: CODE COMPLETE, CONTAINERIZATION PENDING

API Gateway (Node.js):

Code: ✅ Complete (2,960 bytes server.js)
Dependencies: ✅ Complete (package.json with 13 dependencies)
Dockerfile: ✅ Complete (529 bytes)
Status: Ready to containerize
Port: 8000


Requirement Processor (Python):

Code: ✅ Complete (158 lines main.py, 4,298 bytes)
Dependencies: ❌ Missing requirements.txt
Dockerfile: ❌ Empty (0 bytes)
Status: Code tested manually, needs containerization
Port: 8001


Tech Stack Selector (Python):

Code: ✅ Complete (158 lines main.py, 4,278 bytes)
Dependencies: ❌ Missing requirements.txt
Dockerfile: ❌ Empty (0 bytes)
Status: Ready for containerization
Port: 8002


Architecture Designer (Python):

Code: ✅ Complete (158 lines main.py, 4,298 bytes)
Dependencies: ❌ Missing requirements.txt
Dockerfile: ❌ Empty (0 bytes)
Status: Ready for containerization
Port: 8003


Code Generator (Python):

Code: ✅ Complete (158 lines main.py, 4,228 bytes)
Dependencies: ❌ Missing requirements.txt
Dockerfile: ❌ Empty (0 bytes)
Status: Ready for containerization
Port: 8004


Test Generator (Python):

Code: ✅ Complete (158 lines main.py, 4,228 bytes)
Dependencies: ❌ Missing requirements.txt
Dockerfile: ❌ Empty (0 bytes)
Status: Ready for containerization
Port: 8005


Deployment Manager (Python):

Code: ✅ Complete (158 lines main.py, 4,268 bytes)
Dependencies: ❌ Missing requirements.txt
Dockerfile: ❌ Empty (0 bytes)
Status: Ready for containerization
Port: 8006



Management Scripts: 100% COMPLETE
Located in scripts/setup/:

✅ start.sh (7,790 bytes) - Main startup script (FIXED Redis auth)
✅ stop.sh (1,812 bytes) - Stop all services
✅ status.sh (4,561 bytes) - Check system status
✅ validate-phase1.sh (5,455 bytes) - Phase 1 validation
✅ logs.sh (1,060 bytes) - View service logs
✅ dev.sh (3,391 bytes) - Development mode
✅ cleanup.sh (1,701 bytes) - Clean up resources

Project Configuration Files

docker-compose.yml:

Infrastructure services: ✅ Complete
Application services: ❌ Not added yet
Networks and volumes: ✅ Complete


Environment Configuration:

✅ .env file with all required variables
✅ Database passwords configured
✅ Service configurations


Database Schemas: ✅ Complete PostgreSQL, MongoDB, Redis setup


🔧 KNOWN ISSUES AND SOLUTIONS
✅ RESOLVED ISSUES

Redis Authentication Issue:

Problem: Startup script couldn't connect to Redis
Root Cause: Script missing password authentication
Solution: Fixed startup script to use redis-cli -a redis_secure_2024 ping
Status: ✅ RESOLVED


Docker Compose Version Warning:

Problem: Obsolete version attribute warning
Status: ⚠️ Cosmetic issue, doesn't affect functionality



⏳ PENDING ISSUES TO ADDRESS

Python Service Containerization:

Issue: Missing requirements.txt and Dockerfiles for 6 Python services
Impact: Cannot start services with docker-compose
Solution Needed: Create standardized requirements.txt and Dockerfiles


Docker Compose Service Definitions:

Issue: Application services not defined in docker-compose.yml
Impact: Cannot start full system with single command
Solution Needed: Add 7 service definitions to docker-compose.yml




📋 DETAILED NEXT STEPS
IMMEDIATE ACTIONS (Next 1-2 Hours)
Step 1: Create Requirements Files
All Python services use the same dependencies:
fastapi==0.104.1
uvicorn==0.24.0
loguru==0.7.2
pydantic==2.11.4
Step 2: Create Dockerfiles
Standardized Dockerfile template for Python services:
dockerfileFROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY src/ ./src/
EXPOSE 800X
CMD ["uvicorn", "src.main:app", "--host", "0.0.0.0", "--port", "800X"]
Step 3: Add Services to docker-compose.yml
Add definitions for all 7 application services with proper networking, dependencies, and environment variables.
Step 4: Test Complete System
Run ./scripts/setup/start.sh to start all 11 services (4 infrastructure + 7 application).
Step 5: Run Phase 1 Validation
Execute ./scripts/setup/validate-phase1.sh to confirm Phase 1 completion.
PHASE 1 COMPLETION CRITERIA

✅ All 4 infrastructure services healthy
⏳ All 7 application services starting successfully
⏳ API Gateway routing to all microservices
⏳ Health endpoints responding on all services
⏳ Service-to-service communication established
⏳ Phase 1 validation script passing 100%


🎛️ DETAILED SERVICE SPECIFICATIONS
Infrastructure Services

PostgreSQL Database

Image: postgres:15
Port: 5432
Database: dev_pipeline
User: pipeline_admin
Password: pipeline_password
Health: ✅ Confirmed working
Tables: 8 main tables for project state management


Redis Cache

Image: redis:7-alpine
Port: 6379
Password: redis_secure_2024
Persistence: AOF enabled
Health: ✅ Confirmed working with authentication
Usage: Caching, sessions, pub/sub


MongoDB Document Store

Image: mongo:7
Port: 27017
User: pipeline_user
Password: pipeline_password
Health: ✅ Confirmed working
Usage: Code templates, generated projects


RabbitMQ Message Queue

Image: Custom (automated-dev-pipeline-rabbitmq)
AMQP Port: 5672
Management UI: 15672
User: pipeline_admin
Password: rabbit_secure_2024
Health: ✅ Confirmed working
Plugins: Management, Prometheus, Federation



Application Services

API Gateway (api-gateway)

Technology: Node.js + Express
Port: 8000
Dependencies: 13 packages (express, cors, redis, etc.)
Features: JWT auth, rate limiting, WebSocket, service discovery
Code Status: ✅ Complete (2,960 bytes)
Container Status: ✅ Ready


Requirement Processor (requirement-processor)

Technology: Python + FastAPI
Port: 8001
Purpose: Natural language processing, PRD generation
Code Status: ✅ Complete (158 lines, 4,298 bytes)
Container Status: ⏳ Needs Dockerfile + requirements.txt


Tech Stack Selector (tech-stack-selector)

Technology: Python + FastAPI
Port: 8002
Purpose: AI-powered technology selection
Code Status: ✅ Complete (158 lines, 4,278 bytes)
Container Status: ⏳ Needs Dockerfile + requirements.txt


Architecture Designer (architecture-designer)

Technology: Python + FastAPI
Port: 8003
Purpose: System architecture design, database schema generation
Code Status: ✅ Complete (158 lines, 4,298 bytes)
Container Status: ⏳ Needs Dockerfile + requirements.txt


Code Generator (code-generator)

Technology: Python + FastAPI
Port: 8004
Purpose: Multi-framework code generation
Code Status: ✅ Complete (158 lines, 4,228 bytes)
Container Status: ⏳ Needs Dockerfile + requirements.txt


Test Generator (test-generator)

Technology: Python + FastAPI
Port: 8005
Purpose: Automated test generation (unit, integration, E2E)
Code Status: ✅ Complete (158 lines, 4,228 bytes)
Container Status: ⏳ Needs Dockerfile + requirements.txt


Deployment Manager (deployment-manager)

Technology: Python + FastAPI
Port: 8006
Purpose: Local and cloud deployment automation
Code Status: ✅ Complete (158 lines, 4,268 bytes)
Container Status: ⏳ Needs Dockerfile + requirements.txt




🗃️ DATABASE ARCHITECTURE
PostgreSQL Tables (dev_pipeline database)

projects: Main project entity with status tracking
tech_stack_decisions: Technology selection results
system_architectures: Architecture design artifacts
code_generations: Generated code tracking
test_results: Test execution results
deployment_logs: Deployment history
service_health: Service monitoring
project_state_transitions: Audit trail

MongoDB Collections

code_templates: Framework-specific templates
framework_configs: Technology configurations
generated_projects: Complete project storage
ai_prompts: AI prompt templates

Redis Data Structures

Cache Keys: API responses, computed results
Session Data: User session management
Pub/Sub Channels: Real-time updates
Queue Data: Background task processing


🔗 INTEGRATION POINTS
Current Integrations

Docker Network: All services on pipeline_network
Service Discovery: Via API Gateway routing
Health Monitoring: All services expose /health endpoints
Logging: Centralized logging with loguru

Planned Integrations

Claude API: Natural language processing, code generation
CloudtopiAA API: Cloud deployment and infrastructure
n8n Workflows: Service orchestration
Git APIs: Repository management (Gitea/GitLab)


🚨 CRITICAL SUCCESS FACTORS

Infrastructure Stability: ✅ ACHIEVED - All 4 services operational
Service Containerization: 🔄 IN PROGRESS - Need to complete Python services
Inter-service Communication: ⏳ PENDING - Need service mesh setup
Error Handling: ⏳ PENDING - Need comprehensive error recovery
Performance: ⏳ PENDING - Need sub-30-minute generation time
Quality Gates: ⏳ PENDING - Need production-ready code standards


🎯 PROJECT CONTEXT RESTORATION CHECKLIST
When resuming this project, verify:
Environment Check

✅ Are we in the /Users/yasha/Documents/Tech4biz-Code-Generator/automated-dev-pipeline directory?
✅ Do all 7 services exist with proper code files?
✅ Is docker-compose.yml present with infrastructure services?
✅ Are database scripts in place?
✅ Can we run ./scripts/setup/start.sh successfully?

Infrastructure Verification

✅ PostgreSQL: Accessible on localhost:5432
✅ Redis: Accessible with password on localhost:6379
✅ MongoDB: Accessible on localhost:27017
✅ RabbitMQ: Management UI on http://localhost:15672

Code Status Verification

✅ API Gateway: Complete with Dockerfile
✅ Python Services: All have 158-line main.py files
❌ Python Services: Missing requirements.txt and Dockerfiles
❌ docker-compose.yml: Missing application service definitions

Next Session Action Plan

Create requirements.txt for all 6 Python services
Create Dockerfiles for all 6 Python services
Add service definitions to docker-compose.yml
Test complete system startup
Run Phase 1 validation
Begin Phase 2 planning (n8n + AI integration)


📍 CURRENT POSITION SUMMARY
Phase 1 Status: 85% Complete

Infrastructure: 100% Operational ✅
Application Code: 100% Complete ✅
Containerization: 15% Complete (1/7 services) 🔄
Integration: 0% Complete ⏳

Immediate Goal: Complete Phase 1 by containerizing all application services
Next Milestone: Phase 1 validation passing 100% → Begin Phase 2 AI Services Integration
Time Estimate to Phase 1 Completion: 2-3 hours
Overall Project Progress: Week 1.8 of 12-week timeline