A SIMPLE SEARCH ENGINE FROM SCRATCH

Summary from the TLDR Web Dev newsletter

A simple search engine can be built using word embeddings, representing words as vectors in a multi-dimensional space to capture their meaning. The search engine embeds blog posts and queries, then ranks posts based on cosine similarity to the query embedding, with cosine similarity measuring the angle between the vectors. To avoid downloading a large word2vec file, the implementation shown in the post splits the data into smaller files and uses HTTP Range requests for selective data retrieval.