Data Augmenter Phase 1 Implementation - Complete Three-Phase Architecture

Summary

Why Care

Implementation

Changes Made

Core Application Structure

• Next.js 14 Setup: Initialized with App Router and Tailwind CSS
• Project Structure: Organized components, pages, and utilities
• Dependencies: Added zustand, papaparse, lucide-react for state management, CSV parsing, and icons

Phase 1: Import & Record Management

• src/app/page.js: Main Record Collector page with comprehensive record management
• src/components/RecordCollector.js: Core component with CSV import, record display, search, and statistics
• src/components/ImportModal.js: Modal for CSV file upload with drag-and-drop support
• src/components/RecordCard.js: Individual record display with augmentation status indicators
• src/components/SearchBar.js: Real-time search functionality
• src/components/DeleteConfirmModal.js: Confirmation dialog for record deletion
• src/lib/csvParser.js: Robust CSV parsing with quoted field handling
• src/lib/store.js: Zustand store for record management and augmentation data

Phase 2: AI Augmentation

• src/app/augment/page.js: AI augmentation page with record selection
• src/components/AugmentPage.js: Main augmentation interface with Perplexity AI integration
• src/components/PerplexityConfig.js: Configuration panel for API keys, prompts, and settings
• src/components/AugmentationResults.js: Results display with markdown rendering and export
• API Integration: Real Perplexity AI API calls with simulation mode toggle
• Prompt System: Custom prompt editor with placeholder replacement

Phase 3: Export

• src/app/export/page.js: Export page for data selection and remote push
• src/components/ExportPage.js: Export interface with record selection and statistics
• Export Functionality: CSV export with augmentation data, simulated remote push

Navigation & Layout

• src/app/layout.js: Root layout with global navigation
• src/components/Navigation.js: Global navigation bar with three-phase routing
• src/app/globals.css: Global styles with Tailwind CSS integration

Documentation

• RecordCollector.md: Comprehensive project documentation with three-phase architecture
• Changelog System: Established changelog structure for tracking changes

Technical Details

State Management Architecture

// Zustand Store Structure
{
  records: Array<Record>,
  selectedRecord: Record | null,
  addRecords: (records: Record[]) => void,
  deleteRecord: (id: string) => void,
  deleteAllRecords: () => void,
  updateRecordAugmentation: (id: string, data: AugmentationData) => void
}

Record Data Model

interface Record {
  id: string;
  name: string;
  industry: string;
  size: string;
  location: string;
  annual_revenue: number;
  website: string;
  contact_email: string;
  augmentationResults?: AugmentationData;
  lastAugmented?: string;
}

AI Integration Features

• Perplexity AI API: Real HTTP requests with proper error handling
• Simulation Mode: Toggle between real API and simulated responses
• Prompt Templates: Customizable prompts with placeholder replacement
• Batch Processing: Multi-record augmentation with progress tracking
• Result Storage: Augmentation results stored in Zustand store

Responsive Design Implementation

• Mobile-First: Responsive breakpoints (768px, 1024px)
• Grid System: Dynamic column layouts (1-3 columns based on screen size)
• Component Patterns: Consistent card, button, and modal designs
• Accessibility: Proper ARIA labels and keyboard navigation

CSV Processing

• Robust Parsing: Handles quoted fields, embedded commas, escaped quotes
• Data Validation: Required field checking and type conversion
• Error Handling: Comprehensive error messages and validation
• Export Functionality: Full data export with augmentation results

Integration Points

Component Dependencies

• RecordCard: Displays both original and augmented data
• Navigation: Seamless routing between three phases
• Store Integration: Centralized state management across all components
• Modal System: Consistent modal patterns for import, delete, and configuration

API Integration

• Perplexity AI: REST API integration with authentication
• Error Handling: Graceful failure handling and user feedback
• Rate Limiting: Simulated delays and proper request management
• Configuration: API key management and feature toggles

Data Flow

1. Import: CSV → Parse → Validate → Store
2. Augment: Select → Configure → Process → Store Results
3. Export: Select → Format → Export/Push

Documentation

Project Structure

src/
├── app/
│   ├── layout.js (Global navigation)
│   ├── page.js (Record Collector)
│   ├── augment/page.js (AI Augmentation)
│   └── export/page.js (Data Export)
├── components/
│   ├── RecordCollector.js (Main record management)
│   ├── AugmentPage.js (AI augmentation interface)
│   ├── ExportPage.js (Export and remote push)
│   ├── RecordCard.js (Individual record display)
│   ├── PerplexityConfig.js (AI configuration)
│   ├── AugmentationResults.js (Results display)
│   ├── ImportModal.js (CSV import dialog)
│   ├── Navigation.js (Global navigation)
│   └── [Other UI components]
└── lib/
    ├── store.js (Zustand store)
    └── csvParser.js (CSV parsing utilities)

Key Features Implemented

• Three-Phase Workflow: Import → Augment → Export
• AI Integration: Perplexity AI with real/simulation modes
• Data Management: Complete CRUD operations for records
• Export Capabilities: CSV export with augmentation data
• Responsive Design: Mobile-first responsive layout
• State Management: Centralized Zustand store
• Error Handling: Comprehensive error management
• User Experience: Intuitive navigation and feedback

Performance Optimizations

• Lazy Loading: Components loaded on demand
• Debounced Search: Real-time search optimization
• Efficient Rendering: React optimization patterns
• Memory Management: Proper cleanup and state management

Security Considerations

• API Key Management: Secure input fields with show/hide toggle
• Data Validation: Input sanitization and validation
• Client-Side Only: No server-side data storage (current implementation)
• Error Boundaries: Graceful error handling without data exposure

Conclusion