Project Overview
I built an AI-powered Retrieval-Augmented Generation (RAG)
application that allows users to explore and search through 140+ co-op and internship reports
using natural language queries. The system processes PDF and DOCX documents, splits them into
chunks, converts them into semantic embeddings using transformer models, and stores them in a
FAISS vector index for semantic search. When a user asks a question, the application retrieves
the most relevant internship experiences and uses OpenAI GPT models to generate grounded
responses related to interview preparation, technical skills, workplace experiences, and career
advice. The project also includes an interactive Streamlit interface for searching and exploring
student co-op insights.