@datagica/fast-index v0.1.0

Datagica Fast-Index

A library to lookup if a word is inside an index, even if the spelling is a bit different.

Usage

Installation

$ npm install @datagica/fast-index --save

Building the index

import "FastIndex" from "@datagica/fast-index";

const index = new FastIndex({

  // fields to be indexed
  fields: [
    'label',
    'aliases'
  ],

  // a custom spelling generation function
  spellings: (map, word) => {
    // replace "le " or "el " by "the " with an arbitrary similarity score
   // of 0.5 (you can choose any value between 0 and 1)
    map.set(word.replace(/(?:le|el) /gi, 'the '), 0.5)
  }
})

// now we load some dataset
index.loadSync([
  { label: 'the chef', type: 'movie' },

  // duplicate entries are supported and will be returned in the results
  { label: 'the chef', type: 'book' },

  // duplicates inside an entry are simply skipped
  { label: 'el chef', aliases: [ 'el chef' ] }
]);


// side-note: here is the internal representation of the data inside the index:
[ [ 'the chef',
    [ { value: { label: 'the chef', type: 'movie' }, score: 1 },
      { value: { label: 'the chef', type: 'book' }, score: 1 },
      { value: { label: 'el chef', type: 'unknow', aliases: [ 'el chef' ] },
        score: 0.5 } ] ],
  [ 'el chef',
    [ { value: { label: 'el chef', type: 'unknow', aliases: [ 'el chef' ] },
        score: 1 } ] ] ]

Querying the index

const matches = index.get("le chef");

// this will output
[
  { value: { label: 'the chef', type: 'movie' },
    score: 0.5 },

  { value: { label: 'the chef', type: 'book' },
    score: 0.5 },

  // note how "el chef" has a lower score, although it would be closer using
  // a distance function. That's because we choose a naive spelling function
  // that converts everything into a single locale (english).
  // a better function would be more fine-tuned and store each locale
  // individually
  { value: { label: 'el chef',  type: 'unknow', aliases: [ 'el chef' ] },
    score: 0.25 }
]

History

Problem

The original algorithm used for fuzzy matching entities in all Datagica projects (@datagica/fuzzy-index) was based on a lookup inside a tree of possible spellings, using a Finite State Levenshtein Transducer. This was nice because it allowed us to match a university name (for instance) even if it was spelled a bit differently eg (universidad instead of university).

However for huge datasets this proved quite slow, incompatible with real-time lookup, as it searched more alternative spellings than necessary.

Solution

The new Fast Index simply converts its inputs into a simple representation of the word, where punctuation, accents, useless spaces etc.. have been removed.

These transformations are quite opinionated, but some can be opted-out.

In addition to these default transforms, Fast-Index also gives you a way to define custom alternative spellings.

For instance, you can tell Fast-Index to automatically convert:

• de -> of
• ité -> ity

Now, if the input word is université de Poudlard it will match university of Poudlard!

When using the spelling generator, you decide yourself which distance score should be allocated. This helps the Fast-Index picks the best match it find later.