JobSearchAI

A comprehensive job search automation platform that helps job seekers discover, track, and apply to jobs efficiently. Built with FastAPI (backend) and Next.js (frontend).

🎯 Overview

JobSearchAI transforms the job search process from reactive (customizing resumes for jobs you find) to proactive (discovering jobs, tracking applications, and automating the application process).

Core Features

1. 🔍 Hunter (Job Discovery) - Search and discover jobs from multiple sources
2. 📋 Tracker (Kanban Board) - Track applications through stages with drag-and-drop
3. 🤖 Resume Customization - AI-powered resume tailoring for specific job descriptions
4. 📧 Outreach - Automated email generation and contact finding
5. 🤝 Referrals (Coming in Phase 4) - Network-powered job discovery

🏗️ Architecture

Unified Application Structure

• Backend: FastAPI with SQLAlchemy (PostgreSQL/SQLite)
• Frontend: Next.js 15 with TypeScript and Tailwind CSS
• Database: Single unified database for all features
• Deployment: Single deployment (one backend, one frontend, one database)
• Authentication: JWT-based secure accounts and multi-tenancy

Project Structure

JobSearchAI/
├── backend/                 # FastAPI backend
│   ├── app/
│   │   ├── api/endpoints/  # API routes (jobs, resumes, search, outreach, auth, etc.)
│   │   ├── core/           # Security and config
│   │   ├── db/             # Database configuration
│   │   ├── models/         # SQLAlchemy models
│   │   ├── schemas/        # Pydantic schemas
│   │   └── services/       # Business logic
│   ├── pdf_generator/       # LaTeX PDF generation
│   ├── main.py             # FastAPI app entry point
│   └── requirements.txt    # Python dependencies
├── frontend/               # Next.js frontend
│   ├── src/
│   │   ├── app/            # Next.js app router pages
│   │   ├── components/     # React components
│   │   ├── services/       # API client functions
│   │   └── types/          # TypeScript types
│   ├── package.json        # Node.js dependencies
│   └── ...
└── docs/                   # Documentation and roadmap

🚀 Quick Start

Docker Deployment (Recommended)

The easiest way to run JobSearchAI is using Docker Compose.

1. Prerequisites:

   • Docker and Docker Compose installed.

2. Build and Run:

   docker-compose up --build

   This will start:

   • PostgreSQL: Database for all data
   • Backend: FastAPI server (Port 8000)
   • Frontend: Next.js app (Port 3000)

3. Access the Application:

   • Frontend: http://localhost:3000
   • Backend API Docs: http://localhost:8000/docs

Manual Setup

Backend Setup

1. Navigate to backend directory:

   cd backend

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables: Create a .env file based on .env.example.

4. Start the backend server:

   uvicorn main:app --reload --port 8000

Frontend Setup

1. Navigate to frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Set up environment variables: Create a .env.local file:

   NEXT_PUBLIC_API_BASE_URL=http://127.0.0.1:8000

4. Start the development server:

   npm run dev

📊 Current Status

✅ Phase 1-4: Core Features (COMPLETE)

• Tracker (Kanban), Hunter (Discovery), Bot (Automation), Referrals foundation.

✅ Phase 5: Platform & Security (COMPLETE)

• Authentication: JWT-based login/registration.
• Multi-tenancy: Data isolated by user at the database level.
• Protected Routes: Frontend AuthGuard implemented.

✅ Phase 6: Integration & Polish (COMPLETE)

• LinkedIn Browser Extension: Save jobs directly from LinkedIn.
• Gmail Integration: Auto-sync job updates from your inbox.
• Production Readiness: Docker + PostgreSQL configuration.
• Professional Suite: Analytics dashboard and interview tracking.

🛠️ Technology Stack

Backend

• FastAPI - Modern Python web framework
• SQLAlchemy - ORM for database operations
• Google Gemini - AI for resume customization, email generation, and contact finding
• LaTeX - PDF generation
• JSearch API - Job search aggregation
• Apify Client - For LinkedIn contact search

Frontend

• Next.js 15 - React framework with App Router
• TypeScript - Type-safe JavaScript
• Tailwind CSS - Utility-first CSS
• dnd-kit - Drag and drop for Kanban board
• react-hot-toast - Toast notifications

📝 Environment Variables

Backend (.env)

# Required
GEMINI_API_KEY=your_gemini_api_key

# Optional - Job Search
JSEARCH_API_KEY=your_jsearch_api_key

# Optional - Database
DATABASE_URL=sqlite:///./jobsearchai.db

# Optional - AWS S3
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_REGION=us-east-1
S3_BUCKET_NAME=your-bucket-name

Frontend (.env.local)

NEXT_PUBLIC_API_BASE_URL=http://127.0.0.1:8000

🚢 Deployment

Docker Deployment (Recommended)

The easiest way to run JobSearchAI is using Docker Compose.

1. Prerequisites:

   • Docker and Docker Compose installed.

2. Configure Environment Variables:

   • Create a .env file in the backend/ directory with your API keys (see Backend Setup).

3. Build and Run:

   docker-compose up --build

4. Access the Application:

   • Frontend: http://localhost:3000
   • Backend API Docs: http://localhost:8000/docs

Manual Deployment

See nextSteps.md Section 11 for detailed manual deployment instructions.

📚 Documentation

• Project Roadmap: See nextSteps.md for detailed phases and features
• Database Setup: See docs/DATABASE_SETUP.md for detailed database configuration and initialization
• S3 Integration: See README_S3_INTEGRATION.md

🤝 Contributing

This is a personal project, but suggestions and improvements are welcome!

📄 License

This project is for educational and demonstration purposes.

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Note: This project supports full authentication and multi-tenancy. Each user's data is isolated and secured.