Smart ATS System using RAG

Smart Applicant Tracking System (ATS) that uses advanced NLP and RAG (Retrieval-Augmented Generation) that can search for the best candidates from a vector database of resumes and recommend the best one using a Large Language Model LLM (Google Gemini)

Key Points

Collected data from several resources as resume pdf documents using file scrappers, resume images using OCR model, text from CSV files using Pandas into a Vector Database using Chroma DB.
Built a retrieval system that retrieved most similar documents to the job description.
Embedded an LLM model to collect and reformat the retrieved documents and the job description to recommend the best candidate fit the job.
Designed a user friendly graphical interface using Streamlit with a integrated job description samples to try and testing the app.

📌 Overview

This project enhances the traditional ATS by:

Parsing and understanding resumes using AI
Matching resumes with job descriptions
Answering questions based on stored CV data using a RAG-based QA pipeline

🧠 Tech Stack

Graphical Interface: Streamlit
Backend: Python
Vector Store: ChromaDB
LLM: Google Gemini
Embeddings: sentence-transformers
Other Libraries: pandas, langchain, sentence_transformers.

📂 Project Structure

Smart-ATS/
├── .streamlit/ # Streamlit config files
├── Data/ # Data-related folders
│ ├── job_description/ # Sample or scraped job description texts
│ └── vector_db/ # Vector database files (Chroma DB)
├── images/ # Visual assets and screenshots
├── notebooks/ # Jupyter notebooks for experimentation
├── .gitattributes # Git settings
├── README.md # Project documentation
├── Retriever.py # Core retrieval logic for RAG
├── requirements.txt # Python dependencies
└── st_app.py # Streamlit app entry point