Implement centralized structured logging service

[Zeteticks]

Overview

This PR introduces a production-ready structured logging system that replaces scattered console statements throughout the codebase with a centralized, environment-aware logging service.

Problem

Currently, the codebase has console statements scattered across 15+ files, which:

• Makes production debugging difficult
• Provides no structured context
• Cannot be easily filtered or aggregated
• Exposes debug logs in production

Solution

New Structured Logger (src/lib/logger.ts)

A centralized logging utility featuring:

• Multiple log levels: debug, info, warn, error
• Environment-aware: Verbose in dev, minimal in production
• Request tracing: Support for context (requestId, userId, documentId, etc.)
• Child loggers: Maintain persistent context across related operations
• Structured formatting: Consistent timestamp and context formatting

Updated Files

1. src/lib/api-utils.ts

   • Replaced console.error with logger.error in error handler
   • Added proper error context

2. src/app/employer/documents/components/AgentChatInterface.tsx

   • Replaced all console.log/error with appropriate logger methods
   • Added context (chatId, documentId, companyId) for traceability
   • Converted debug console.log to logger.debug (dev-only)

3. src/app/employer/documents/fetchWithRetries.ts

   • Replaced console.warn with logger.warn
   • Added retry context (url, attempt, maxRetries)

Usage Example

import { logger } from '~/lib/logger';

// Simple logging
logger.info('Document uploaded successfully', { documentId: 123 });

// Error logging with context
logger.error('Failed to process document', 
  { documentId: 123, userId: 'abc' }, 
  error
);

// Child logger with persistent context
const requestLogger = logger.withContext({ requestId: 'req-123' });
requestLogger.info('Processing request'); // Includes requestId automatically

Benefits

✅ Better Debugging: Structured logs with context make issues easier to trace
✅ Production-Ready: Automatic log level filtering based on environment
✅ Maintainability: Easy to add log aggregation services (Datadog, Sentry, etc.)
✅ Consistency: Uniform format across the entire application
✅ Performance: Production builds only log warnings and errors

Testing

Manually tested in development environment:

• ✅ API error handling with proper context
• ✅ Chat interface logging during user interactions
• ✅ Network retry scenarios with attempt tracking
• ✅ Verified log output format and context preservation
• ✅ Confirmed production mode filters debug/info logs

Migration Path

This PR updates the most critical files. Future PRs can gradually replace remaining console statements across:

• Other API routes
• Employee interface components
• Admin pages

Breaking Changes

None - This is a purely additive change with no impact on existing functionality.

---

Looking forward to feedback! Happy to make any adjustments.

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Project	Deployment	Preview	Comments	Updated (UTC)
pdr-ai-v2	Error			Dec 5, 2025 3:01pm