flexsearch VS probly-search

Full-text search is powered by FlexSearch and Nextra will index all of your pages at build time ⚡.

I have a web page that reads in various .md files and displays them as HTML. The app uses the marked library to convert the markdown into HTML for display. I create a flexsearch search index out of the raw text values from the documents (raw text is gathered using DOMParser over all HTML elements) so that user can search for keywords in the docs and get back a table of results. The order of operations and search index code looks like:

I am trying to get flexsearch (a lib written in js) up and running in a TS project and found a working example here. I downloaded the project and ran 'yarn add flexsearch' and also 'yarn add @/types/flexsearch' since I know that you need a special index.d.ts file to convert the JS to TS properly.... however the code errors out during the Index object creation with the message.

Try using a proper browser search like Lunr or Flexsearch

Current version of FlexSearch (0.7.2) is not typo tolerant, see https://github.com/nextapps-de/flexsearch/issues/118

Very nice! Seems to perform very well. I'm curious, have you compared Fuse with other search engines? Like flex search or elasticlunr? Why did you choose fuse ?

I actually looked into this term before localeCompare(): Full Text Search. It's pretty heavy duty. In JavaScript, this can come in the form of a library dependency like FlexSearch. Far too bulky for the humble sorting task I have at hand.

Javascript seems to have a comprehensive in memory solution https://github.com/nextapps-de/flexsearch

Another option that was often suggested to me was building the search index with a library such as https://github.com/nextapps-de/flexsearch and distribute the index than to the client and handle it one the client. But yeah sounds like a lot of overhead and I haven't tried it.

I have spent my last two weeks learning Rust, and by doing so I created a Search Engine library. The purpose of this is to enable users to perform normalized scoring that scores documents between 0 and 1 (1 would mean that the query is exactly matching the document). I developed the algorithm initially in Javascript 2 years ago. I was curious about the performance of a "low level" language (coming from purely Java/Python/Javascript background) and how you can use it with Web assembly, so I thought it would be a great idea to get started with Rust! (I am so far very pleased) Demo: https://quantleaf.github.io/probly-search-demo/ Source: https://github.com/quantleaf/probly-search

Library source code: https://github.com/quantleaf/probly-search Demo source code: https://github.com/quantleaf/probly-search-demo