Written by Ramon Gebben

Levi (GitHub: cshum/levi, License: MIT, npm: levi)

Levi is a streaming full-text search for Node.js and browsers which used LevelDB for storage. The search implementation is done by using TF-IDF and cosine similarity, and it’s provided with configurable text processing pipelines: Tokenizer, Porter Stemmer and Stopwords filter.

Levi is built on LevelUP which is a fast, asynchronous, transactional storage interface. By default it uses LevelDB on Node.js, when in running in the browser it uses IndexedDB. Levi supports with a variety of LevelDOWN compatible backends.

In addition, Levi provides relevancy scoring for live changing data using TF-ICF - a TF-IDF approximation based on existing corpus. Such scoring matches are comparably close to TF-IDF when existing corpus is sufficiently large, with significantly better performance O(N) instead of O(N^2).

Let’s take a look at the API. To get started, we need to create a new Levi instance. We do that like this:

import levi from 'levi';

const lv = levi('db')

The text processing pipeline levi.tokenizer(), levi.stemmer(), levi.stopword() are required for indexing. These are exposed as ginga plugins so that they can be swapped for different language configurations.

Now that we have an instance we can use it to talk to the API like this:

lv.put('a', 'Lorem Ipsum is simply dummy text.', err => {
    // ...

// object fields as value
lv.put('b', {
  id: 'b',
  title: 'Lorem Ipsum',
  body: 'Dummy text of the printing and typesetting industry.'
}, (err) => {

Or retrieve it like:

lv.get('b', res => {
    // res here;

To actually search we need to use Levi’s main interface which would be searchStream. We use that like this.

lv.searchStream('lorem ipsum').toArray(function (results) { ... }) // highland method

lv.searchStream('lorem ipsum', {
  fields: { title: 10, '*': 1 } // title field boost. '*' means any field

lv.searchStream('lorem ipusm', {
  fields: { title: 1 }, // title only

// ltgt
lv.searchStream('lorem ipusm', {
  gt: '!posts!',
  lt: '!posts!~'

// document as query
  title: 'Lorem Ipsum',
  body: 'Dummy text of the printing and typesetting industry.'

Because I do not fully understand everything that is going on here, I need to refer you to the extensive documentation on the GitHub page. The reason I still mention this library is that I understand from the explanation a colleague gave me that this library will have a significant impact on the speed of your text search.