USING VECTORIZE TO BUILD AN UNREASONABLY GOOD SEARCH ENGINE IN 160 LINES OF CODE

Summary from the TLDR Web Dev newsletter

Creating a semantic search engine involves using an embedding model to convert text into vectors, storing these vectors in a vector database, and then querying the database using the embedding of the user's search query to find semantically similar documents. This article is a step-by-step guide on setting this up.