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Building a full-text search engine in 150 lines of Python code (2021)
Full-text search is everywhere. From finding a book on Scribd, a movie on Netflix, toilet paper on Amazon, or anything else on the web through Google (like [how to do your job as a software engineer](https://localghost.dev/2019/09/everything-i-googled-in-a-week-as-a-professional-software-engineer/)), you've searched vast amounts of unstructured data multiple times today. What's even more amazing, is that you've even though you searched millions (or [billions](https://www.worldwidewebsize.com/)) of records, you got a response in milliseconds. In this post, we are going to build a basic full-text search engine that can search across millions of documents and rank them according to their relevance to the query in milliseconds, in less than 150 lines of code!
From finding a book on Scribd, a movie on Netflix, toilet paper on Amazon, or anything else on the web through Google (like how to do your job as a software engineer), you’ve searched vast amounts of unstructured data multiple times today. Back of the book index Practically, what this means is that we’re going to create a dictionary where we map all the words in our corpus to the IDs of the documents they occur in. You can find all the code on Github, and I’ve provided a utility function that will download the Wikipedia abstracts and build an index.
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