The Digital Press

A blog post demonstrates how to build a simple search engine using word embeddings and cosine similarity, starting from scratch with minimal code. It covers embedding blog posts, handling queries, and implementing an efficient web-based search interface.

Why it matters: Using word embeddings improves search relevance by capturing semantic meaning beyond keyword matching.

The big picture: This project shows how lightweight, interpretable search engines can be built without relying on heavyweight external tools or servers.

Quick takeaway: The search engine uses word2vec embeddings summed per document and ranks results by cosine similarity to the query vector.

Commenters say: Many appreciate the clear, beginner-friendly approach and code transparency, while some suggest exploring larger embedding vocabularies and TF-IDF weighting for better results.