Hello HN!

I became frustrated with the unpredictible/poor match quality and opaqueness of "relevance scores" in existing fuzzy and fulltext search libs, so I tried something different and this is the result. The main selling point is the result quality / ordering, with best-in-class memory overhead and excellent performance being bonuses. The API is pretty stable at this point, but looking for feedback before committing to 1.0.

TL;DR

The test corpus is a 4MB json file with 162k words/phrases, so give it a second for initial download. You can also drag/drop your own text/json corpus into the UI to try it against your own dataset.

Live demo/compare with a few other libs (there are many more in the codebase, in various states of completion, WIP):

https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uF...

In isolation for perf assessment:

https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uF...

To increase fuzziness and get broader results, try setting intraMax=1 (core) and enable outOfOrder (userland):

https://leeoniya.github.io/uFuzzy/demos/compare.html?libs=uF...

Also play with the sortPreset selector to swap out the default Array.sort() for one in userland that prioritizes typehead-ness (the resultset remains identical).

Still TODO:

  - Example of stripping diacritics
  - Example of using non-latin charsets
  - Example of prefix-caching to improve typeahead perf even further
  - Example of poor man's document search (matching multiple object properties)

That's all, thanks!

user:	mo_po2
created:	August 7, 2022
karma:	13