job-forge/README.md

Job Forge - AI-Powered Job Application Assistant

Job Forge is a Python/FastAPI web application prototype that leverages AI to streamline the job application process through automated document generation, application tracking, and intelligent job matching.

🚀 Quick Start

# Clone the repository
git clone https://github.com/yourusername/job-forge.git
cd job-forge

# Set up environment variables
cp .env.example .env
# Edit .env with your API keys (Claude, OpenAI, JWT secret)

# Start development environment
cd docker
docker compose up -d

# Access the applications
# Frontend: http://localhost:8501
# Backend API: http://localhost:8000
# Database: localhost:5432

📚 Documentation Navigation

🏗️ Architecture & Planning

• System Architecture - Complete system overview and component breakdown
• API Specification - Comprehensive API endpoints and examples
• Database Design - Schema design with RLS policies and performance optimization
• MVP Checklist - Development progress tracking

🛠️ Development

• Development Setup - Environment setup and configuration
• Getting Started Guide - Week-by-week implementation roadmap
• Coding Standards - Python/FastAPI best practices
• Implementation Patterns - Code templates and examples

🧪 Testing & Quality

• Testing Strategy - Comprehensive testing approach with pytest
• QA Procedures - Quality assurance workflows
• Manual Testing - User workflow validation procedures

🚢 Deployment & Infrastructure

• Server Deployment - Direct server deployment procedures
• Docker Setup - Containerization and orchestration
• Environment Configuration - Production environment setup
• Monitoring & Logging - Observability and alerting

🔒 Security & Operations

• Security Guidelines - Production security measures
• Backup Procedures - Data backup and recovery
• Git Workflow - Branching strategy and collaboration

🤖 AI Integration

• AI Services Setup - Claude and OpenAI API integration
• Document Generation - Automated resume and cover letter creation
• Job Matching - AI-powered job recommendation system

🏛️ Technology Stack

Backend

• FastAPI - Modern Python web framework
• PostgreSQL 16 - Database with pgvector for AI embeddings
• SQLAlchemy - ORM with async support
• Alembic - Database migrations
• Pydantic - Data validation and serialization

Frontend

• Dash - Interactive web applications
• Mantine Components - Modern UI component library
• Plotly - Data visualization
• Bootstrap - Responsive design framework

AI & ML

• Claude API - Document generation and analysis
• OpenAI API - Embeddings and completions
• pgvector - Vector similarity search
• Sentence Transformers - Text embeddings

Development & Deployment

• Docker - Containerization
• Docker Compose - Development orchestration
• pytest - Testing framework
• Black - Code formatting
• Ruff - Fast Python linter

🎯 Core Features

✨ AI Document Generation

• Automated cover letter creation based on job descriptions
• Resume optimization and tailoring
• Professional formatting and styling
• Multiple export formats (PDF, DOCX)

📊 Application Tracking

• Comprehensive job application management
• Application status tracking and timeline
• Interview scheduling and notes
• Follow-up reminders and notifications

🔍 Job Matching

• AI-powered job recommendation system
• Skills gap analysis and suggestions
• Salary insights and market data
• Company culture and fit analysis

👥 Multi-User Support

• Secure user authentication and authorization
• Data isolation with PostgreSQL RLS
• User profile and preference management
• Team collaboration features

🏃 Development Workflow

1. Planning Phase - Technical Lead defines architecture and specifications
2. Implementation Phase - Full-Stack Developer builds features
3. Quality Assurance - QA Engineer validates and tests
4. Deployment Phase - DevOps Engineer handles deployment

📋 Project Status

• Project architecture and database design
• Development environment setup
• API specification and documentation
• Core backend API implementation
• Frontend Dash application
• AI service integrations
• Testing suite implementation
• Production deployment

🤝 Team & Agents

Job Forge uses specialized AI agents for development:

• Technical Lead - Architecture decisions and technical guidance
• Full-Stack Developer - FastAPI backend and Dash frontend implementation
• QA Engineer - pytest testing and quality assurance
• DevOps Engineer - Docker deployment and server infrastructure

See CLAUDE.md for complete team orchestration documentation.

📖 Documentation Categories

📚 Getting Started

Perfect for new developers joining the project:

• Development Setup
• Getting Started Guide
• MVP Checklist

🔧 Implementation

For active development work:

• API Specification
• Database Design
• System Architecture

💡 Future Features

For managing new feature ideas and scope:

• Feature Management Process
• Feature Ideas
• Under Review
• Approved Features

🧪 Quality Assurance

For testing and validation:

• Testing Strategy
• QA Procedures
• Manual Testing

🚀 Deployment

For production deployment:

• Server Deployment
• Docker Setup
• Security Guidelines

🛡️ Security

Job Forge implements comprehensive security measures:

• Authentication: Secure user authentication with JWT tokens
• Authorization: Role-based access control (RBAC)
• Data Isolation: PostgreSQL Row Level Security (RLS)
• API Security: Rate limiting and input validation
• Encryption: Data encryption at rest and in transit

📈 Performance

Optimized for performance and scalability:

• Database: Optimized queries with proper indexing
• API: Async FastAPI for high concurrency
• Caching: Redis for session and API response caching
• CDN: Static asset delivery optimization
• Monitoring: Application and infrastructure monitoring

🧪 Testing

Comprehensive testing strategy:

• Unit Tests: 80%+ coverage with pytest
• Integration Tests: API and database testing
• End-to-End Tests: User workflow validation
• Performance Tests: Load and stress testing
• Security Tests: Vulnerability scanning and validation

📊 Monitoring & Analytics

Built-in observability:

• Application Monitoring: Error tracking and performance metrics
• Infrastructure Monitoring: Server and database health
• User Analytics: Feature usage and user behavior
• Security Monitoring: Threat detection and response

🤖 AI Integration

Seamless AI service integration:

• Claude API: Advanced document generation and analysis
• OpenAI API: Embeddings and text completions
• Vector Search: Semantic job matching with pgvector
• Error Handling: Robust fallback and retry mechanisms

📱 User Experience

Modern and intuitive interface:

• Responsive Design: Mobile-first responsive layout
• Interactive Components: Rich Dash components with Mantine
• Real-time Updates: Live status updates and notifications
• Accessible: WCAG 2.1 AA compliance
• Performance: Fast loading and smooth interactions

🔧 Development Tools

Optimized development experience:

• Hot Reload: Real-time code changes with Docker
• Code Quality: Black formatting and Ruff linting
• Type Safety: Full type hints with mypy validation
• Debugging: Comprehensive logging and debugging tools
• Testing: Fast test execution with pytest

📞 Support & Contributing

• Issues: Report bugs and request features via GitHub Issues
• Documentation: Comprehensive documentation in docs/ folder
• Code Style: Follow established Python/FastAPI best practices
• Testing: Maintain test coverage above 80%
• Reviews: All changes require code review and testing

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Ready to start building? 🚀

1. Follow the Development Setup guide
2. Check the Getting Started roadmap
3. Review the API Specification
4. Start with the MVP Checklist

For team coordination and agent management, see CLAUDE.md.