Building a Search Engine from Scratch – 1 (unconventionalthinker.vercel.app)

In a recent article, the author details their journey of building a search engine from scratch, starting with the implementation of the Term Frequency (TF) algorithm. This method is fundamental to semantic search, enabling searches based on the meaning of words rather than mere word occurrences. By illustrating the concept with relatable analogies, the author explains how TF organizes word counts from documents into a structured format, effectively showcasing its simplicity and intuitive nature. The author demonstrates this with Python code that processes text files from the 20 NewsGroup Dataset, providing a practical guide for readers interested in grasping the foundational algorithms that power search systems. This exploration is significant for the AI/ML community as it underscores the importance of understanding basic search algorithms, especially as AI-driven semantic search becomes more prevalent. While TF is easy to implement, the author acknowledges its limitations, such as the challenge posed by common filler words that can skew search results. This sets the stage for future discussions on more advanced algorithms that can improve accuracy and relevance in search queries, highlighting the ongoing evolution of search technology in an increasingly AI-centric landscape.