The project builds a semantic search system for memes using Word2Vec embeddings to map natural language queries to relevant results. Users enter text queries and the system retrieves matching memes based on semantic similarity. The frontend is built with Streamlit, and the core embedding logic leverages word vector representations for retrieval.