semantic search, a method that goes beyond keyword matching to understand the context and intent of user queries for more accurate results. This process involves Natural Language Processing (NLP), which converts text into numerical vectors representing meaning, with closer vectors indicating greater similarity. The Universal Sentence Encoder (USE) is highlighted for its role in transforming sentences into these semantic vectors, while FAISS (Facebook AI Similarity Search) is presented as a tool for efficiently indexing and querying large collections of these vectors to retrieve relevant information. The practical application of these techniques is demonstrated using the 20 Newsgroups dataset, illustrating the steps from data preprocessing to vectorization and FAISS-powered searching.