Data Augmentation Workflow with Microfrontends

1. Executive Summary

2. Background & Motivation

• Problem: Traditional monolithic data processing workflows are difficult to scale, maintain, and extend with new processing capabilities
• Current Limitations: Tight coupling between processing stages, difficulty in independent deployment, and challenges in team collaboration on different workflow components
• Why Now: The need for flexible, AI-assisted content processing that can adapt to different data types and processing requirements while enabling distributed development

3. Goals & Non-Goals

Goals

• Create a modular, scalable data augmentation workflow using microfrontends
• Enable independent development and deployment of workflow components
• Provide seamless integration between processing stages
• Support AI-assisted content enhancement and review processes
• Maintain data consistency and traceability throughout the workflow

Non-Goals

• Real-time streaming data processing (batch processing focus)
• Complex data transformation beyond augmentation and enhancement
• Direct database management (relies on existing data layer)

4. Technical Design

High-Level Architecture

1. RecordCollector - Initial data collection and ingestion
2. PromptTemplateManager - Template management for AI prompts
3. RequestReviewer - Review and validation of processing requests
4. ResponseReviewer - Quality assurance for AI-generated responses
5. HighlightCollector - Extraction and collection of key insights
6. InsightAssembler - Final assembly and synthesis of processed data
7. {{Additional Component}} - {{To be defined}}

Detailed Design

Module Federation Architecture

• Each application is independently deployable
• Shared dependencies managed through module federation
• Common UI components and utilities shared across applications
• Event-driven communication between microfrontends

Docker & Monorepo Integration

• Containerized Development: Docker provides consistent development environments across all microfrontends
• Monorepo Structure: The entire lossless-monorepo is containerized with proper submodule management
• Unified Build Process: Single Dockerfile handles content and site submodules with pnpm workspace configuration
• Environment Isolation: Each microfrontend can be developed and tested in isolated Docker containers
• Deployment Consistency: Docker ensures identical runtime environments from development to production

Data Flow

API Specifications

• RESTful APIs for inter-service communication
• GraphQL endpoints for complex data queries
• WebSocket connections for real-time status updates
• Standardized data schemas across all components

Error Handling

• Graceful degradation when individual microfrontends are unavailable
• Retry mechanisms for failed processing stages
• Comprehensive logging and error tracking
• Rollback capabilities for failed augmentation attempts

5. Implementation Plan

Phase 1: Core Infrastructure

• Set up Docker development environment with monorepo support
• Configure module federation framework
• Implement base microfrontend shell with containerized builds
• Create shared component library accessible across Docker containers
• Establish communication protocols between containerized services

Phase 2: Individual Applications

• Develop and deploy each microfrontend application
• Implement data processing logic
• Create user interfaces for each component
• Establish testing frameworks

Phase 3: Integration & Optimization

• End-to-end workflow testing
• Performance optimization
• User experience refinement
• Documentation and training materials

Dependencies

• Module federation framework (Webpack 5+)
• Shared UI component library
• Common data schemas and validation
• AI processing services integration
• Docker containerization platform
• pnpm workspace configuration for monorepo management
• Git submodule support for content and site repositories

Testing Strategy

• Unit tests for individual microfrontend logic
• Integration tests for inter-service communication
• End-to-end workflow testing
• Performance and load testing

6. Alternatives Considered

Monolithic Architecture

• Pros: Simpler deployment, easier debugging
• Cons: Difficult to scale, tight coupling, single point of failure
• Decision: Rejected due to scalability and maintainability concerns

Microservices with Traditional Frontend

• Pros: Backend scalability, clear service boundaries
• Cons: Frontend remains monolithic, limited UI modularity
• Decision: Rejected in favor of full microfrontend approach

7. Open Questions

• Specific AI service integration patterns and APIs
• Data persistence strategy across microfrontends
• User authentication and authorization across applications
• Performance monitoring and analytics implementation
• Deployment orchestration and CI/CD pipeline design
• Docker registry strategy for microfrontend container distribution
• Container orchestration approach (Docker Compose vs Kubernetes)

8. Appendix

Glossary

• Microfrontend: Independently deployable frontend application that focuses on a specific business capability
• Module Federation: Webpack feature that allows sharing of code and dependencies between separate builds
• Data Augmentation: Process of enhancing existing data with additional information or AI-generated content

References

• Micro Frontends Architecture
• Webpack Module Federation Documentation
• Individual application specifications (linked above)

Revision History

• v0.0.0.1 (2025-07-24): Initial draft with basic application list
• v0.0.0.1 (2025-08-09): Applied specification template structure