After spending months applying to countless jobs, I realized how time-consuming and frustrating it can be to tailor resumes for every application and gauge how well they match a job description. That’s how the idea for ResumeMatcher came to me—a personal project designed to help both job seekers and recruiters quickly identify the best matches.
ResumeMatcher is an AI-powered tool that uses a Retrieval-Augmented Generation (RAG) pipeline to intelligently compare resumes with job descriptions, producing a match score and actionable insights.
Here’s how it works:
Data Extraction & Cleaning – ResumeMatcher accepts PDF, DOCX, and TXT files, extracting and cleaning text for analysis.
Embeddings & FAISS Indexing – Text data is transformed into embeddings using OpenAI’s models. These embeddings are stored in a FAISS index for lightning-fast similarity searches.
Intelligent Matching – When a resume is compared to a job description, ResumeMatcher retrieves the most relevant context from the FAISS index and feeds it, along with the resume and job description, into a large language model (GPT-3.5).
Scoring & Explanation – The system generates a match score (0-100) and bullet-point explanations that highlight why a candidate is a strong (or weak) fit.
ResumeMatcher offers both a command-line interface (CLI) for developers and a Streamlit web interface for recruiters and job seekers, making it accessible and user-friendly.
After building this tool for my own job search, I realized it could help many others save time and make smarter applications. Try ResumeMatcher today at: https://resumematcher-rag.streamlit.app/
Top comments (0)