Building a Search Engine from Scratch – 1
The article discusses the author's journey in building a search engine from scratch, focusing on the implementation of various search algorithms. It highlights the concept of semantic search and introduces the Term Frequency (TF) algorithm as a foundational method. The author aims to provide insights and resources for those interested in understanding search systems better.
- ▪The author has a background in implementing a vector search database for an AI app.
- ▪Semantic search allows for searching based on the meaning of words rather than just the words themselves.
- ▪The TF algorithm simplifies the process of searching through documents by counting the frequency of words.
Opening excerpt (first ~120 words) tap to expand
May 15, 2026 Building a Search Engine (Part 1) I have always been intrigued by search engines and how they work. My first foray into search was when I implemented a vector search database to add RAG (Retrieval Augmented Generation) into an AI app I built last year. Basically, vector search is semantic search. Semantic means you search based on the meaning of words. It's why you can search for money and Google can bring out search pages that talk about the US dollar. But semantic search is a more recent feature of search popularized thanks to AI systems. The earliest search systems used algorithms that were more focused on just the words themselves. So, in my spare time, I set out to understand three of those algorithms and implement them for myself.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Vercel.
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