AiToS

Developer Note

Hi there! 👋 This is a small experimental project I hacked together over a weekend to turn a shell script into a web application.

I’m not a web developer by trade (my background is in other fields) so most of the web app code was generated with GitHub Copilot and refined by me. The real work was in validating and adjusting what Copilot produced. I also designed the initial LLM prompts myself and iterated on them a bit using ChatGPT.

The app runs fine on Linux (and should on macOS). With Docker Compose, you might get it working on Windows via WSL, but that’s outside the scope of what I tested.

⚠️ Important: This project is not under active development. I don’t plan to add features, fix bugs, update dependencies, or review pull requests. It was a personal learning exercise. I especially enjoyed working with Tailwind CSS and experimenting with Ollama’s API.

For users

• Outputs are for educational purposes only: no guarantees, positive or negative.
• Suggestions come from publicly available LLM models and may not reflect reality. Always double-check the results.
• Resume/CV parsing quality depends on the structure of your document. (Tip: try Apache Tika to preview parsing results)
• Please don’t blame me for anything. 😉

For developers

• This project is MIT licensed. Fork and modify as you wish.
• Treat it as a starting point or a learning resource. It is not a finished product.

Introduction

A comprehensive full-stack application for analyzing resumes and CVs against job descriptions using AI models via Ollama. AiToS simulates Applicant Tracking System (ATS) behavior to help job seekers optimize their resumes.

Features

• Backend (Node.js + Express + SQLite/Prisma)

  • Upload and parse resumes (PDF/DOCX → text extraction)
  • Create and manage job descriptions
  • AI-powered ATS analysis using Ollama models (Gemma3, Qwen3, GPT-OSS)
  • Comprehensive scoring system (keyword matching, formatting safety, parsing clarity)
  • RESTful API with OpenAPI documentation
  • Full CRUD operations for CVs, job descriptions, and reports
  • Docker support with embedded services

• Frontend (React + Vite)

  • Clean, responsive web interface
  • Drag-and-drop file upload for CVs
  • Job description management
  • Real-time analysis results with detailed scoring
  • Report management and comparison
  • Model selection and management

• CLI Tool

  • Standalone command-line interface for quick analysis
  • Direct integration with Ollama models
  • Support for PDF and DOCX files
  • Optional Poppler integration for PDF processing

Quick Start

Option 1: Docker (Recommended - Everything Included)

# Start all services (includes Ollama, Tika, backend, frontend)
docker-compose up --build

# Access the application:
# - Frontend: http://localhost:3001
# - Backend API: http://localhost:3000
# - Ollama: http://localhost:11434

That's it! Docker includes all dependencies and AI models.

Option 2: Local Development

Prerequisites:

• Node.js 18+ and Yarn
• Ollama installed locally

# 1. Install dependencies
yarn install

# 2. Setup environment (copy and modify if needed)
cp backend/.env.example backend/.env

# 3. Start Ollama and install models
ollama serve
ollama pull gemma3:latest
ollama pull qwen3:latest
ollama pull gpt-oss:latest

# 4. Initialize database
yarn db:init

# 5. Start development servers
yarn dev

Available at:

• Frontend: http://localhost:3001
• Backend: http://localhost:3000

Docker Commands

# Start all services
yarn docker:up

# Run in background
yarn docker:up:bg

# Stop services
yarn docker:down

# View logs
yarn docker:logs

CLI Usage (Optional)

# Quick command-line analysis
./cli/aitos.sh resume.pdf job_description.txt gemma3

Project Structure

aitos/
├── backend/           # Node.js API server
│   ├── src/
│   │   ├── controllers/     # API route handlers
│   │   ├── services/        # Business logic & integrations
│   │   ├── routes/          # Express route definitions
│   │   └── utils/           # Database utilities
│   ├── prisma/        # Database schema and migrations
│   ├── docker/        # Docker configuration
│   ├── db/            # Legacy database schema files
│   └── package.json
├── frontend/          # React web application
│   ├── src/
│   │   ├── components/      # React components
│   │   ├── services/        # API service layer
│   │   └── App.jsx
│   ├── docker/        # Frontend Docker configuration
│   └── package.json
├── cli/               # Command-line interface
│   ├── aitos.sh       # Standalone CLI script
│   └── README.md      # CLI documentation
└── package.json       # Root workspace configuration

API Documentation

The backend includes comprehensive OpenAPI documentation available at backend/openapi.yaml. Access the interactive Swagger UI at: http://localhost:3000

Key API Endpoints

CV Management:

• POST /api/cv - Upload CV (multipart/form-data)
• GET /api/cv - List all CVs with previews
• GET /api/cv/:id - Get CV details and full text
• PUT /api/cv/:id - Update CV content
• DELETE /api/cv/:id - Delete CV

Job Description Management:

• POST /api/jd - Create job description
• GET /api/jd - List all job descriptions with previews
• GET /api/jd/:id - Get job description details
• PUT /api/jd/:id - Update job description content
• DELETE /api/jd/:id - Delete job description

Analysis & Reports:

• POST /api/analyze - Analyze CV against JD with specified model
• GET /api/report - List all analysis reports
• GET /api/report/:id - Get detailed report with scores
• POST /api/report/:id/reanalyze - Reanalyze with different model
• DELETE /api/report/:id - Delete report

Model Management:

• GET /api/ollama/tags - List available Ollama models
• POST /api/ollama/pull - Download/install new models

Scoring System

AiToS provides comprehensive ATS simulation with multiple scoring metrics:

• Parsing Clarity Score (0-100): How well can ATS systems parse the resume
• Keyword Match Score (0-100): Alignment with job description requirements
• Formatting Safety Score (0-100): ATS-friendly formatting assessment
• Overall Score (0-100): Model's holistic assessment
• Weighted Overall Score: Calculated as (0.4 × keyword_match + 0.3 × parsing_clarity + 0.3 × formatting_safety)

Additional insights include missing keywords, suggested improvements, and company-specific interview questions.

Docker Deployment

The application supports full containerization with Docker Compose:

# Complete stack deployment (recommended)
docker-compose up --build

# Background deployment
docker-compose up -d --build

# View logs
docker-compose logs -f [service_name]

# Scale services
docker-compose up --scale backend=2

# Clean shutdown
docker-compose down --volumes

The Docker setup includes:

• Backend: Node.js API with Prisma ORM
• Frontend: React application with Vite
• Ollama: AI model service with persistent storage
• Automatic networking: Services can communicate using service names

Available Models

Supported Ollama models for analysis:

• gemma3: Google's Gemma 3 model (lightweight, fast)
• qwen3: Alibaba's Qwen 3 model (balanced performance)
• gpt-oss: Open-source GPT variant (comprehensive analysis)

Install models using:

ollama pull gemma3:latest
ollama pull qwen3:latest
ollama pull gpt-oss:latest

License

MIT License