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RoleFit - Intelligent Resume & Cover Letter Generation Platform

RoleFit is a sophisticated backend service that leverages AI to generate tailored resumes and cover letters based on job descriptions. It provides a comprehensive platform for managing user profiles, skills, experiences, and automatic document generation with intelligent content optimization.

📋 Table of Contents

🎯 Overview

RoleFit is a FastAPI-based backend service designed to help job seekers create customized resumes and cover letters. The platform intelligently analyzes job descriptions and generates optimized documents that match the job requirements while maintaining authenticity.

Problem Statement

Job seekers spend considerable time manually tailoring their resumes for each job application. RoleFit automates this process using AI to:

  • Parse and understand job descriptions
  • Extract relevant user skills and experiences
  • Generate customized, ATS-friendly resumes
  • Create compelling cover letters tailored to specific jobs
  • Manage multiple document versions

🌟 Key Features

User Management

  • User registration and authentication
  • Secure JWT-based authorization
  • User profile management with customizable settings
  • Account authentication with email verification

Resume Management

  • Multiple resume templates (Sidebar, Bold, Minimalist styles)
  • Automatic resume generation from user profile data
  • Resume extraction from uploaded PDF files
  • Dynamic resume updates based on job descriptions
  • PDF generation in multiple formats
  • ATS-optimized resume structure

Cover Letter Generation

  • AI-powered cover letter creation
  • Multiple template styles (Minimal, Professional, Creative)
  • Job description-based content generation
  • Dynamic PDF generation with formatting
  • Cover letter caching for performance

Job Description Management

  • Import and store job descriptions
  • Automated JD parsing and analysis
  • Skill and requirement extraction
  • Support for multiple job descriptions per user
  • Job description caching and search

Profile Data Management

The system manages comprehensive user profile information:

  • Profile: Basic user information and preferences
  • Experience: Work history with detailed descriptions
  • Education: Academic qualifications and certifications
  • Skills: Professional skills with proficiency levels
  • Tools/Technologies: Technical tools and programming languages
  • Projects: Portfolio projects with descriptions
  • Publications: Research papers, articles, and publications
  • Achievements: Certifications, awards, and recognitions

Intelligent Features

  • AI-Powered Content Generation: Uses Groq AI for intelligent content synthesis
  • Smart Filtering: Filters user data based on job requirements
  • Caching Layer: Redis-based caching for performance optimization
  • Real-time Updates: WebSocket support for live document generation status
  • Async Processing: Celery for background task processing

🏗️ Architecture

High-Level Architecture

┌─────────────────────────────────────────────────────────────┐
│                    FastAPI Web Server                       │
│                    (Port 8000)                              │
└──────────────────┬──────────────────────────────────────────┘
                   │
        ┌──────────┼──────────┬──────────┐
        │          │          │          │
    ┌───▼──┐  ┌───▼──┐  ┌───▼──┐  ┌───▼──┐
    │Users │  │Resume│  │Cover │  │  Job │
    │      │  │Letter│  │Letter│  │ Desc │
    └───┬──┘  └───┬──┘  └───┬──┘  └───┬──┘
        │         │         │         │
        └─────────┼─────────┼─────────┘
                  │
        ┌─────────┼─────────┐
        │         │         │
    ┌───▼──┐  ┌──▼──┐  ┌──▼──┐
    │  DB  │  │Redis│  │Celery
    │(PgSQL)  │Cache│  │Worker
    └───────┘  └─────┘  └──────┘

Request Flow

  1. Client Request → FastAPI Router
  2. Authentication → JWT Validation
  3. Business Logic → Service Layer
  4. Data Access → Database/Cache
  5. Long Operations → Celery Queue
  6. Response → JSON Response or WebSocket Update

🛠️ Tech Stack

Backend Framework

  • FastAPI (0.135.2) - Modern async web framework
  • Uvicorn - ASGI server
  • Pydantic (2.12.5) - Data validation and settings management
  • Python (3.9+)

Database & Caching

  • PostgreSQL (16-Alpine) - Primary database
  • Redis (7-Alpine) - Caching layer and message broker
  • SQLAlchemy - ORM for database operations

AI & Content Generation

  • Groq (1.2.0) - AI API for intelligent content generation
  • PDFMiner.six (20251230) - PDF parsing and extraction
  • pdfplumber (0.11.9) - PDF analysis
  • Pillow (12.2.0) - Image processing for PDF generation

Async & Background Jobs

  • Celery - Distributed task queue
  • aioredis (2.0.1) - Async Redis client
  • asyncio - Async runtime

Authentication & Security

  • python-jose (3.5.0) - JWT token handling
  • bcrypt (3.2.0) - Password hashing
  • passlib (1.7.4) - Password utilities
  • cryptography (47.0.0) - Encryption utilities

Utilities

  • python-dotenv - Environment configuration
  • httpx - Async HTTP client
  • email-validator - Email validation
  • PyYAML - Configuration parsing

📁 Project Structure

rolefit-backend/
├── app/
│   ├── api/
│   │   ├── router.py                 # Main API router
│   │   └── v1/                       # API v1 endpoints
│   │       ├── auth/                 # Authentication endpoints
│   │       ├── user/                 # User management
│   │       ├── profile/              # User profile management
│   │       ├── resume/               # Resume generation endpoints
│   │       ├── cover_letter/         # Cover letter endpoints
│   │       ├── job_description/      # Job description endpoints
│   │       ├── experience/           # Work experience endpoints
│   │       ├── academics/            # Education endpoints
│   │       ├── skill/                # Skills management
│   │       ├── tools/                # Tools/technologies management
│   │       ├── project/              # Portfolio projects
│   │       ├── publications/         # Publications management
│   │       ├── resume_extractor/     # PDF resume extraction
│   │       ├── content/              # Content retrieval
│   │       ├── health/               # Health check endpoint
│   │       └── websocket/            # WebSocket connections
│   │
│   ├── core/
│   │   ├── AppError.py               # Custom exception handling
│   │   ├── celery_app.py             # Celery configuration
│   │   ├── cors.py                   # CORS setup
│   │   ├── logger.py                 # Logging configuration
│   │   ├── redis_keys.py             # Redis key constants
│   │   ├── validation_error.py       # Validation utilities
│   │   ├── expectations.py           # Expectation validations
│   │   ├── grok_const.py             # Groq AI constants
│   │   ├── sarvam_const.py           # Sarvam AI constants
│   │   └── resume_colors.py          # Resume styling constants
│   │
│   ├── db/
│   │   ├── db.py                     # SQLAlchemy setup
│   │   └── redis_db.py               # Redis connection
│   │
│   ├── models/
│   │   ├── User.py                   # User model
│   │   ├── Profile.py                # User profile model
│   │   ├── Experience.py             # Work experience model
│   │   ├── Academic.py               # Education model
│   │   ├── Skill.py                  # Skills model
│   │   ├── Tool.py                   # Tools/technologies model
│   │   ├── Project.py                # Portfolio projects model
│   │   ├── Publication.py            # Publications model
│   │   ├── Achievement.py            # Achievements/certifications
│   │   ├── JobDescription.py         # Job description model
│   │   ├── GeneratedDocument.py      # Generated resumes/letters
│   │   ├── UserSkill.py              # User-skill relationship
│   │   └── UserTool.py               # User-tool relationship
│   │
│   ├── schema/
│   │   ├── auth.py                   # Authentication schemas
│   │   ├── pdf_resume.py             # PDF resume schemas
│   │   ├── CoverLetterData.py        # Cover letter data schemas
│   │   ├── Academic.py               # Academic schemas
│   │   ├── Experience.py             # Experience schemas
│   │   ├── Skill.py                  # Skill schemas
│   │   ├── Tool.py                   # Tool schemas
│   │   ├── Project.py                # Project schemas
│   │   ├── Publication.py            # Publication schemas
│   │   ├── JobDescription.py         # Job description schemas
│   │   └── GeneratedDocument.py      # Generated document schemas
│   │
│   ├── response/
│   │   ├── user_responses.py         # User response schemas
│   │   ├── profile_responses.py      # Profile response schemas
│   │   ├── experience_responses.py   # Experience responses
│   │   ├── academic_responses.py     # Academic responses
│   │   ├── skill_responses.py        # Skill responses
│   │   ├── tool_responses.py         # Tool responses
│   │   ├── project_responses.py      # Project responses
│   │   ├── publication_responses.py  # Publication responses
│   │   ├── GenerateDocument_responses.py  # Document responses
│   │   └── job_description_response.py   # Job description responses
│   │
│   ├── helpers/
│   │   ├── redis_cache_helpers.py    # Redis caching utilities
│   │   ├── db_helpers.py             # Database helper functions
│   │   ├── pdf_helpers.py            # PDF generation utilities
│   │   ├── jd_parser.py              # Job description parsing
│   │   ├── filter_jd.py              # Job description filtering
│   │   ├── filter_jd_sync.py         # Sync JD filtering
│   │   ├── resume_prompt.py          # Resume generation prompts
│   │   ├── cover_letter_prompt.py    # Cover letter prompts
│   │   ├── build_pdf.py              # Base PDF builder
│   │   ├── build_pdf_bold.py         # Bold resume template
│   │   ├── build_pdf_minimalist.py   # Minimalist resume template
│   │   ├── build_pdf_sidebar.py      # Sidebar resume template
│   │   ├── build_cover_letter_pdf.py # Cover letter PDF builder
│   │   ├── build_cover_letter_bold.py # Bold cover letter template
│   │   ├── build_cover_letter_minimal.py # Minimal cover letter template
│   │   ├── celery_helpers.py         # Celery task helpers
│   │   ├── grok_ai_headers.py        # Groq API headers
│   │   ├── sarvam_ai_headers.py      # Sarvam API headers
│   │   └── validation_schemas.py     # Data validation
│   │
│   ├── dependency/
│   │   └── dependencies.py           # FastAPI dependency injection
│   │
│   ├── tasks/
│   │   └── [Celery async tasks]     # Background job tasks
│   │
│   ├── utils/
│   │   └── [Utility functions]      # General utilities
│   │
│   ├── validators/
│   │   └── [Data validators]        # Validation logic
│   │
│   └── websockets/
│       ├── redis_subscriber.py       # Redis WebSocket subscriber
│       └── [WebSocket handlers]     # Real-time communication
│
├── docker/
│   └── init.sql/                     # Database initialization scripts
│
├── logs/                             # Application logs
│
├── tests/                            # Test suite
│   ├── test_resume_generation.py
│   ├── test_enum_parsing.py
│   ├── test_requirements.txt
│   └── ...
│
├── env/                              # Python virtual environment
│
├── main.py                           # Application entry point
├── requirements.txt                  # Python dependencies
├── docker-compose.yml                # Docker compose configuration
├── Dockerfile                        # Docker image build
├── run_celery_worker.py              # Celery worker runner
├── run_celery_beat.py                # Celery beat scheduler runner
└── debug_celery.py                   # Celery debugging script

🚀 Setup & Installation

Prerequisites

  • Python 3.9 or higher
  • Docker and Docker Compose (for containerized setup)
  • PostgreSQL 16 (if not using Docker)
  • Redis 7 (if not using Docker)
  • Git

Local Setup (without Docker)

1. Clone the Repository

git clone https://github.com/yourusername/rolefit.git
cd rolefit/rolefit-backend

2. Create Virtual Environment

python -m venv env
source env/bin/activate  # On Windows: env\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file in the rolefit-backend directory:

# Database
DATABASE_URL=postgresql://rolefit:secret@localhost:5432/rolefit

# Redis
REDIS_URL=redis://localhost:6379

# JWT
SECRET_KEY=your-secret-key-here
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30

# AI APIs
GROQ_API_KEY=your-groq-api-key
GROQ_MODEL=mixtral-8x7b-32768

# Email (if needed)
SMTP_SERVER=smtp.gmail.com
SMTP_PORT=587
SMTP_USER=your-email@gmail.com
SMTP_PASSWORD=your-app-password

# Celery
CELERY_BROKER_URL=redis://localhost:6379
CELERY_RESULT_BACKEND=redis://localhost:6379

# Application
APP_NAME=RoleFit
DEBUG=True

5. Initialize Database

# Ensure PostgreSQL is running
psql -U rolefit -d rolefit -f docker/init.sql/init.sql

6. Run the Application

uvicorn main:app --reload --host 0.0.0.0 --port 8000

Docker Setup

1. Build and Run with Docker Compose

cd rolefit
docker-compose up -d

This will start:

  • Backend API (http://localhost:8000)
  • PostgreSQL Database (localhost:5432)
  • Redis Cache (localhost:6379)
  • Celery Worker (background tasks)

2. View Logs

docker-compose logs -f backend
docker-compose logs -f celery-worker
docker-compose logs -f postgres

3. Stop Services

docker-compose down

🏃 Running the Application

Development Server

# Standard run
uvicorn main:app --reload

# With specific host and port
uvicorn main:app --host 0.0.0.0 --port 8000 --reload

Celery Worker (Background Tasks)

python run_celery_worker.py
# or
celery -A app.core.celery_app worker -l info

Celery Beat (Scheduled Tasks)

python run_celery_beat.py
# or
celery -A app.core.celery_app beat -l info

Access API Documentation

📡 API Endpoints

Authentication Endpoints (/api/v1/auth)

  • POST /signup - Register new user
  • POST /login - User login with email/password
  • POST /refresh-token - Refresh JWT token
  • POST /logout - User logout

User Endpoints (/api/v1/user)

  • GET / - Get current user profile
  • GET /{user_id} - Get user by ID
  • PUT /{user_id} - Update user information
  • DELETE /{user_id} - Delete user account

Profile Endpoints (/api/v1/profile)

  • GET / - Get user profile
  • POST / - Create profile
  • PUT / - Update profile
  • DELETE / - Delete profile

Resume Endpoints (/api/v1/resume)

  • GET / - Get all resumes
  • POST /generate - Generate resume from profile
  • POST /generate-tailored - Generate tailored resume for job
  • GET /{resume_id}/download - Download resume as PDF
  • PUT /{resume_id} - Update resume
  • DELETE /{resume_id} - Delete resume

Cover Letter Endpoints (/api/v1/cover-router)

  • GET / - Get all cover letters
  • POST /generate - Generate cover letter
  • GET /{letter_id}/download - Download cover letter as PDF
  • PUT /{letter_id} - Update cover letter
  • DELETE /{letter_id} - Delete cover letter

Job Description Endpoints (/api/v1/job-descriptions)

  • GET / - Get all job descriptions
  • POST / - Create/import job description
  • GET /{jd_id} - Get specific job description
  • PUT /{jd_id} - Update job description
  • DELETE /{jd_id} - Delete job description
  • POST /parse - Parse and extract job requirements

Experience Endpoints (/api/v1/experience)

  • GET / - Get all work experiences
  • POST / - Add new experience
  • PUT /{exp_id} - Update experience
  • DELETE /{exp_id} - Delete experience

Education Endpoints (/api/v1/academics)

  • GET / - Get all education records
  • POST / - Add new education
  • PUT /{academic_id} - Update education
  • DELETE /{academic_id} - Delete education

Skills Endpoints (/api/v1/skills)

  • GET / - Get all skills
  • POST / - Add skill
  • PUT /{skill_id} - Update skill
  • DELETE /{skill_id} - Delete skill

Tools/Technologies Endpoints (/api/v1/tools)

  • GET / - Get all tools
  • POST / - Add tool
  • PUT /{tool_id} - Update tool
  • DELETE /{tool_id} - Delete tool

Projects Endpoints (/api/v1/project)

  • GET / - Get all projects
  • POST / - Add project
  • PUT /{project_id} - Update project
  • DELETE /{project_id} - Delete project

Publications Endpoints (/api/v1/publications)

  • GET / - Get all publications
  • POST / - Add publication
  • PUT /{pub_id} - Update publication
  • DELETE /{pub_id} - Delete publication

Resume Extractor Endpoints (/api/v1/resume-extractor)

  • POST /upload - Upload and extract resume from PDF
  • GET /status/{task_id} - Check extraction status

Content Endpoints (/api/v1/content)

  • GET /{content_id} - Get generated content (resume/cover letter)

Health Check Endpoints (/api/v1/health)

  • GET / - Check API health status

WebSocket Endpoints (/api/v1/websocket)

  • WS /connect - Connect to real-time updates

🧩 Core Modules

Authentication Module (app/api/v1/auth)

Handles user authentication, JWT token generation, and password management.

  • Email/password registration
  • JWT-based authentication
  • Secure password hashing with bcrypt
  • Token refresh mechanism

Resume Generation Module (app/api/v1/resume)

Core functionality for resume creation and customization.

  • Features:

    • Multiple resume templates (Sidebar, Bold, Minimalist)
    • Smart resume tailoring based on job descriptions
    • ATS-optimized formatting
    • Real-time PDF generation
    • Version control and storage

  • Templates:

    • Bold: Professional template with emphasis on achievements
    • Sidebar: Modern template with sidebar for quick info
    • Minimalist: Clean and simple design

Cover Letter Generation Module (app/api/v1/cover_letter)

Automated cover letter creation with AI assistance.

  • Features:

    • AI-powered content generation using Groq
    • Multiple writing styles
    • Job description matching
    • PDF generation with professional formatting
    • Caching for performance

  • Templates:

    • Minimal: Concise professional format
    • Bold: Emphasizes achievements
    • Creative: Personalized and engaging style

Job Description Parsing Module (app/api/v1/job_description)

Intelligent parsing and analysis of job descriptions.

  • Features:
    • Automatic skill extraction
    • Requirement analysis
    • Keyword identification
    • Salary range extraction
    • Technology stack detection

Resume Extractor Module (app/api/v1/resume_extractor)

Automated resume parsing from PDF files.

  • Features:
    • PDF parsing and text extraction
    • Information structuring
    • Automatic field detection
    • Data validation
    • Error handling for malformed PDFs

Caching Layer (app/helpers/redis_cache_helpers.py)

Redis-based caching for performance optimization.

  • Features:
    • User authentication cache
    • Resume cache
    • Job description cache
    • Cover letter cache
    • Configurable TTL (Time To Live)

🗄️ Database Schema

Core Tables

Users Table

id: UUID (Primary Key)
email: String (Unique)
password_hash: String
created_at: Timestamp
updated_at: Timestamp
is_active: Boolean
is_verified: Boolean

User Profile

id: UUID (Primary Key)
user_id: UUID (Foreign Key)
first_name: String
last_name: String
phone: String
location: String
headline: String
summary: String
profile_picture_url: String

Experience

id: UUID (Primary Key)
user_id: UUID (Foreign Key)
job_title: String
company: String
employment_type: String
start_date: Date
end_date: Date (nullable)
description: Text
is_current: Boolean

Academic

id: UUID (Primary Key)
user_id: UUID (Foreign Key)
school: String
degree: String
field_of_study: String
start_date: Date
end_date: Date
grade: String (nullable)
activities: Text (nullable)

Skills

id: UUID (Primary Key)
user_id: UUID (Foreign Key)
skill_name: String
proficiency_level: Enum (Beginner, Intermediate, Advanced, Expert)
endorsements: Integer (default: 0)

Tools/Technologies

id: UUID (Primary Key)
user_id: UUID (Foreign Key)
tool_name: String
experience_level: String
years_of_experience: Integer

Projects

id: UUID (Primary Key)
user_id: UUID (Foreign Key)
project_name: String
description: Text
technologies_used: String[] (array)
start_date: Date
end_date: Date (nullable)
project_url: String (nullable)

Job Descriptions

id: UUID (Primary Key)
user_id: UUID (Foreign Key)
job_title: String
company: String
job_description: Text
required_skills: String[] (array)
preferred_skills: String[] (array)
imported_at: Timestamp
saved_at: Timestamp

Generated Documents

id: UUID (Primary Key)
user_id: UUID (Foreign Key)
job_description_id: UUID (Foreign Key, nullable)
document_type: Enum (Resume, CoverLetter)
template_type: String
content_json: JSON
generated_at: Timestamp
file_path: String
status: Enum (Processing, Completed, Failed)

⚙️ Async Processing with Celery

What is Celery?

Celery is a distributed task queue that allows the application to execute long-running operations asynchronously.

Configured Tasks

  1. Resume PDF Generation

    • Generates resume PDF in background
    • Notifies user via WebSocket when complete
    • Stores file for download

  2. Cover Letter PDF Generation

    • Generates cover letter PDF asynchronously
    • Supports multiple templates
    • Real-time progress updates

  3. Resume Extraction from PDF

    • Parses uploaded resume files
    • Extracts and structures information
    • Validates extracted data

  4. Job Description Parsing

    • Parses job postings
    • Extracts skills and requirements
    • Identifies key technologies

Running Celery Components

# Start worker
python run_celery_worker.py

# Start scheduler (for periodic tasks)
python run_celery_beat.py

# Monitor tasks (in another terminal)
celery -A app.core.celery_app events

💾 Caching Strategy

Redis Cache Implementation

The application uses Redis for caching with the following strategy:

  1. Authentication Cache

    • Cache authenticated user objects
    • TTL: 30 minutes
    • Invalidated on logout or password change

  2. User Data Cache

    • Cache user profile, skills, experiences
    • TTL: 15 minutes
    • Invalidated on profile update

  3. Job Description Cache

    • Cache parsed job descriptions
    • TTL: 1 hour
    • Invalidated on JD update

  4. Resume/Cover Letter Cache

    • Cache generated documents
    • TTL: 2 hours
    • Invalidated on content update

Cache Functions

# Get cached value
value = await get_cache(key)

# Set cached value with TTL
await set_cache(key, value, ttl=300)

# Delete cached value
await delete_cache(key)

# Invalidate user cache
await invalidate_user_cache(user_id)

⚙️ Configuration

Environment Variables

Create a .env file with the following variables:

# Database Configuration
DATABASE_URL=postgresql://user:password@localhost:5432/rolefit

# Redis Configuration
REDIS_URL=redis://localhost:6379

# JWT Configuration
SECRET_KEY=your-super-secret-key-change-this
ALGORITHM=HS256
ACCESS_TOKEN_EXPIRE_MINUTES=30

# Groq AI Configuration
GROQ_API_KEY=your-groq-api-key
GROQ_MODEL=mixtral-8x7b-32768

# Application Settings
APP_NAME=RoleFit
DEBUG=False
LOG_LEVEL=INFO

# Celery Configuration
CELERY_BROKER_URL=redis://localhost:6379
CELERY_RESULT_BACKEND=redis://localhost:6379

Application Settings

Key configuration files:

  • app/core/celery_app.py - Celery configuration
  • app/core/cors.py - CORS policy setup
  • app/core/logger.py - Logging configuration
  • app/db/db.py - Database configuration

👨‍💻 Development

Running Tests

# Run all tests
pytest

# Run specific test file
pytest tests/test_resume_generation.py

# Run with coverage
pytest --cov=app tests/

Code Structure Best Practices

  1. Service Layer: Business logic in *_service.py files
  2. Router Layer: API endpoints in *_router.py files
  3. Schema Layer: Data validation in schema/ directory
  4. Response Layer: Response formatting in response/ directory
  5. Models: Database models in models/ directory

Adding New Features

  1. Create model in app/models/
  2. Create schema in app/schema/
  3. Create response schema in app/response/
  4. Create service in app/api/v1/[feature]/
  5. Create router in app/api/v1/[feature]/
  6. Add route to app/api/v1/router.py

Debugging

Enable debug logging:

DEBUG=True
LOG_LEVEL=DEBUG

View logs:

# Docker logs
docker-compose logs -f backend

# Local logs
tail -f logs/app.log

📊 Performance Considerations

  1. Database Queries: Use efficient queries with proper indexing
  2. Caching: Leverage Redis for frequently accessed data
  3. PDF Generation: Offload to Celery workers
  4. File Storage: Store PDFs efficiently with proper cleanup
  5. API Rate Limiting: Consider implementing rate limits for public endpoints

🔐 Security Features

  1. JWT Authentication: Secure token-based authentication
  2. Password Hashing: bcrypt with salt for password security
  3. CORS: Configurable CORS policy
  4. SQL Injection Prevention: SQLAlchemy ORM prevents SQL injection
  5. Input Validation: Pydantic schema validation on all inputs
  6. Error Handling: Custom error handlers prevent information leakage

📝 API Response Format

Success Response

{
  "status": "success",
  "data": {
    "id": "uuid",
    "name": "John Doe"
  }
}

Error Response

{
  "status": "error",
  "error": {
    "code": "ERROR_CODE",
    "message": "Human-readable error message"
  }
}

🤝 Contributing

  1. Create feature branch: git checkout -b feature/feature-name
  2. Commit changes: git commit -m "Add feature"
  3. Push to branch: git push origin feature/feature-name
  4. Create Pull Request

📄 License

[Your License Here]

📞 Support

For issues and questions:

🚀 Deployment

Production Deployment Checklist

  • Set DEBUG=False
  • Update SECRET_KEY with strong random value
  • Configure production database
  • Configure production Redis instance
  • Set up SSL/TLS certificates
  • Configure proper CORS origins
  • Set up logging and monitoring
  • Configure backup strategy
  • Set up CI/CD pipeline
  • Load test the application

Recommended Hosting

  • API Server: AWS ECS, Google Cloud Run, or Heroku
  • Database: AWS RDS PostgreSQL
  • Cache: AWS ElastiCache Redis
  • File Storage: AWS S3
  • Task Queue: Celery with managed Redis

📚 Additional Resources

Version: 1.0.0
Last Updated: May 2026
Maintainer: RoleFit Team

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