MatchCV 🚀

AI-Powered Resume Matching & Candidate Ranking Platform

MatchCV is an AI-driven recruitment platform that helps recruiters upload resumes, extract candidate data, and rank the best candidates using semantic search instead of traditional keyword matching.

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✨ Features

• 📄 Resume Upload – Upload resumes in PDF or DOCX format
• 🔍 Automatic Parsing – Extract email, phone number, skills, and experience
• 🤖 AI Resume Summaries – Generate concise candidate summaries using AI
• 🧠 Semantic Search – Find candidates based on meaning, not just keywords
• 📊 Candidate Ranking – Rank resumes using embeddings, cosine similarity, and keyword scoring
• 🧑‍💼 Recruiter Dashboard – View, filter, and manage candidates in one place

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🛠️ Tech Stack

Frontend

• React.js
• TypeScript
• Tailwind CSS

Backend

• Node.js
• Express.js
• MongoDB

AI

• Google Gemini API
• AI Embeddings
• Cosine Similarity

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🏗️ System Architecture

1. Recruiter uploads resumes (PDF/DOCX)
2. Backend extracts raw text and structured data
3. AI generates resume summaries and embeddings
4. Job requirements are embedded
5. Candidates are ranked using cosine similarity + keyword scoring
6. Results are displayed in a recruiter-friendly dashboard

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🚀 Getting Started

Prerequisites

• Node.js 18+
• MongoDB (local or Atlas)
• Google Gemini API key

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1️⃣ Clone the Repository

git clone https://github.com/your-username/matchcv.git
cd matchcv

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2️⃣ Backend Setup

cd backend
npm install
cp .env.example .env
npm run dev

Backend runs on: http://localhost:5000

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3️⃣ Frontend Setup

cd frontend
npm install
cp .env.example .env
npm run dev

Frontend runs on: http://localhost:5173

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🔐 Environment Variables

Backend (.env)

MONGODB_URI=your_mongodb_connection_string
GEMINI_API_KEY=your_google_gemini_api_key
JWT_SECRET=your_jwt_secret
PORT=5000

Frontend (.env)

VITE_API_URL=http://localhost:5000/api

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📊 Ranking Logic

• AI Embeddings are generated for resumes and job descriptions
• Cosine Similarity measures semantic relevance
• Keyword Scoring boosts important skills and terms
• Final score determines candidate ranking

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🧪 Development Scripts

Backend

npm run dev

Frontend

npm run dev
npm run build
npm run preview

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📈 Future Improvements

• Job description upload & matching
• Recruiter team collaboration
• Feedback-based ranking improvements
• Exportable shortlists
• Enterprise-scale hiring workflows

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📄 License

MIT License

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👤 Author

Built by Sonal Singh An AI-powered full-stack project focused on modern hiring workflows