Feature Request: Comprehensive Production Error Handling and Logging System

[kartikeyg0104]

Description

This feature request proposes the implementation of a comprehensive, production-ready error handling and logging system for the Devr.AI project. The goal is to address critical issues identified during the codebase review and establish robust error management practices across both backend and frontend components.

Problem Statement

Issues Identified:

Inconsistent Error Handling: Different endpoints return errors in various formats, making it difficult for clients to handle errors consistently.
Poor Error Visibility: Errors lack sufficient context, such as correlation IDs or detailed metadata, making it hard to trace the root cause of issues.
Basic Logging: Logging is minimal and lacks structure, rotation, and correlation ID tracking.
No Request Tracking: There is no mechanism to track requests across the system using unique identifiers (e.g., correlation IDs).
Missing Health Monitoring: The system lacks comprehensive health checks for its components.
Frontend Error Management: The frontend lacks robust error handling, retry logic, and user-friendly error messages.
Weak Environment Validation: Environment variables are not validated properly, leading to potential misconfigurations.

Proposed Solution

Backend Improvements

Custom Exception Classes: Structured exception hierarchy with context information.
Error Handling Middleware: Automatic error processing with correlation IDs.
Enhanced Logging: Structured logging with rotation, correlation tracking, and performance monitoring.
Health Check System: Comprehensive health monitoring for all system components.
Configuration Validation: Robust environment variable validation with helpful error messages.

Frontend Improvements

Enhanced API Client: Retry logic, comprehensive error handling, and request tracking.
Custom Error Classes: Structured error objects with correlation IDs and context.
User-Friendly Error Messages: Meaningful error messages for better user experience.
Health Monitoring: Frontend health checks and service status monitoring.

[kartikeyg0104]

I have raised the pull request(#160), you can review it once.

[smokeyScraper]

Not sure whether we require a bit complicated logging and error tracing system for now, but I'll review it once for sure. Thanks!

[kartikeyg0104]

Okay sure, if any changes is needed plz let me know I will do it.

[noobbatman]

Hello! I am very keen to work on this issue

I propose a Simple,Lightweight Architecture to solve this. It doesnt need any external heavy libraries

My Architecture

1. Unified Error Handling (Backend)

   Base Class: A DevrException class that all custom errors inherit from.

   Standardised Response: All errors will return a consistent JSON schema:
   JSON

   {
   "error": {
   "code": "RESOURCE_NOT_FOUND",
   "message": "User with ID 123 not found",
   "request_id": "abc-123-xyz" // Correlation ID
   }
   }

2. Middleware Layer

   RequestIDMiddleware: Assigns a X-Request-ID (UUID) to every incoming request and response headers. This allows us to trace a request from Frontend → Backend → Logs.

   GlobalErrorHandler: Catches all exceptions (both DevrException and unexpected 500s), logs them with the Request ID, and returns the sanitised JSON response.

3. Structured Logging

   I will configure the standard Python logging module to output JSON-formatted logs (including timestamps and Request IDs) to both console and a rotating file handler.

4. Health & Validation

   /health Endpoint: A lightweight endpoint checking DB connectivity.

   Config Validation: Building on my previous work (the Pydantic fix), ensuring all critical variables are checked at startup.

Plan

I propose breaking this into 3 Pull Requests to keep reviews manageable:

Phase 1: Core Infrastructure (Exceptions, Logging Config, RequestID Middleware).

Phase 2: Integration (Global Exception Handler, updating existing routes to use new Exceptions).

Phase 3: Frontend & Health (React API client interceptor to handle standard errors, Health endpoint).

I am ready to start immediately with Phase 1.So i hope you will assign me