Every modern AI search system — from Google to ChatGPT’s retrieval pipeline — works by converting text into numerical vectors and measuring how close those vectors are in high-dimensional space. This technique is called semantic search, and the numerical representations are called vector embeddings. Despite being the backbone of Retrieval-Augmented Generation (RAG), recommendation engines, and intelligent search, virtually every tutorial on the internet implements it in Python. Java developers are left guessing.

This post builds a complete semantic search engine in pure Java — no LangChain4j, no Spring AI, no external dependencies. We implement TF-IDF vectorisation, cosine similarity, and a query engine that ranks documents by meaning rather than keyword matching. By the end, you will understand the exact mathematics that powers every vector database on the market.