MiniSearch

A simple and naive mini search engine in memory using BM25 algorithm.

MiniSearch implements a inverted index (basically a hashmap where terms are keys and values are documents that contains that key.

Installation

Add this line to your application's Gemfile:

gem 'mini_search'

And then execute:

$ bundle

Or install it yourself as:

$ gem install mini_search

BM25 (from wikipedia)

BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document, regardless of their proximity within the document. It is a family of scoring functions with slightly different components and parameters. One of the most prominent instantiations of the function is as follows.

Given a query Q, containing keywords q1....qn the BM25 score of a document D is:

where f(qi, D) is qi's term frequency (tf) in the document D, |D| is the length of the document D in words, and avgdl is the average document length in the text collection from which documents are drawn. k1 and b are free parameters, usually chosen, in absence of an advanced optimization, as k1 in |1.2,2.0| and b = 0.75. IDF(qi) is the IDF (inverse document frequency) weight of the query term qi. It is usually computed as:

where N is the total number of documents in the collection, and n(q) is the number of documents containing qi.

There are several interpretations for IDF and slight variations on its formula. In the original BM25 derivation, the IDF component is derived from the Binary Independence Model.

The above formula for IDF has drawbacks for terms appearing in more than half of the corpus documents. These terms' IDF is negative, so for any two almost-identical documents, one which contains the term may be ranked lower than one which does not. This is often an undesirable behavior, so many applications adjust the IDF formula in various ways:

Each summand can be given a floor of 0, to trim out common terms; The IDF function can be given a floor of a constant e, to avoid common terms being ignored at all; The IDF function can be replaced with a similarly shaped one which is non-negative, or strictly positive to avoid terms being ignored at all.

Inverted Index

MiniSearch implements a inverted index (basically a hashmap where terms are keys and values are documents that contains that key.

Lets take two small documents as examples:

doc1 = 'The domestic dog is a member of the genus Canis, which forms part of the wolf-like canids'
doc2 = 'The cat is a small carnivorous mammal. It is the only domesticated species in the family Felidae and often referred to as the domestic cat.

To create an inverted index we start with an empty hashmap:

ii = {}

Now for a given document we transform its text in tokens (words):

doc1 = ["The", "domestic", "dog", "is", "a", "member", "of", "the", "genus", "Canis,", "which", "forms", "part", "of", "the", "wolf-like", "canids"]
doc2 = ["The", "cat", "is", "a", "small", "carnivorous", "mammal.", "It", "is", "the", "only", "domesticated", "species", "in", "the", "family", "Felidae", "and", "often", "referred", "to", "as", "the", "domestic", "cat."]

We take each term and create use it as a key in are hashmap ii and the value will be a list with all documents containing that term.

def index(doc_id, doc, ii)
  # 1 - tokenizer
  tokens = doc.split(' ')

  tokens.each { |token| ii[token] ||= []; ii[token] << doc_id }
end

ii = {}

index(:doc1, doc1, ii)
index(:doc2, doc2, ii)

puts ii

# {
#   'The'          => [:doc1, :doc2],
#   'domestic'     => [:doc1, :doc2],
#   'dog'          => [:doc1],
#   'is'           => [:doc1, :doc2],
#   'a'            => [:doc1, :doc2],
#   'member'       => [:doc1],
#   'of'           => [:doc1, :doc1],
#   'the'          => [:doc1, :doc2],
#   'genus'        => [:doc1],
#   'Canis,'       => [:doc1],
#   'which'        => [:doc1],
#   'forms'        => [:doc1],
#   'part'         => [:doc1],
#   'wolf-like'    => [:doc1],
#   'canids'       => [:doc1],
#   'cat'          => [:doc2],
#   'small'        => [:doc2],
#   'carnivorous'  => [:doc2],
#   'mammal.'      => [:doc2],
#   'It'           => [:doc2],
#   'only'         => [:doc2],
#   'domesticated' => [:doc2],
#   'species'      => [:doc2],
#   'in'           => [:doc2],
#   'family'       => [:doc2],
#   'Felidae'      => [:doc2],
#   'and'          => [:doc2],
#   'often'        => [:doc2],
#   'referred'     => [:doc2],
#   'to'           => [:doc2],
#   'as'           => [:doc2],
#   'cat.'         => [:doc2]
# }

Now it is ease to perform any search, if we want to get all documents about cat we could simply take the term cat and see the list o documents in it 'cat' => [:doc2], if we want to search for 2 or more terms we can do the same small cat = 'cat' => [:doc2] and 'small' => [:doc2].

Clearly we can improve our index performing some transformations in the tokens before indexing them. For instance we can see we have cat and cat. tokens, we have The and the. lets clean the data before indexing.

Lets change our define an index pipeline that will be called everytime a document is indexed

def index(doc_id, doc, ii)
  # 1 - tokenizer
  tokens = doc.split(' ')

  # 2 - trim
  tokens = tokens.map(&:strip)

  # 3 - downcase all tokens
  tokens = tokens.map(&:downcase)

  # 4 - remove punctuation
  tokens = tokens.map { |token| token.tr(',.!;:', '') }

  tokens.each { |token| ii[token] ||= []; ii[token] << doc_id }

  # ... index
end

With this changes our index would be:

{
  'the' => [:doc1, :doc2],
  'domestic' => [:doc1, :doc2],
  'dog' => [:doc1],
  'is' => [:doc1, :doc2],
  'a' => [:doc1, :doc2],
  'member' => [:doc1],
  'of' => [:doc1],
  'genus' => [:doc1],
  'canis' => [:doc1],
  'which' => [:doc1],
  'forms' => [:doc1],
  'part' => [:doc1],
  'wolf-like' => [:doc1],
  'canids' => [:doc1],
  'cat' => [:doc2],
  'small' => [:doc2],
  'carnivorous' => [:doc2],
  'mammal' => [:doc2],
  'it' => [:doc2],
  'only' => [:doc2],
  'domesticated' => [:doc2],
  'species' => [:doc2],
  'in' => [:doc2],
  'family' => [:doc2],
  'felidae' => [:doc2],
  'and' => [:doc2],
  'often' => [:doc2],
  'referred' => [:doc2],
  'to' => [:doc2],
  'as' => [:doc2]
}

Pretty better now, we could apply other steps like removing some words that are irrelevant for us (stop words), add synonyms for some words but this other changes are specifics from languages.

Usage

First we create an inverted Index

  idx = MiniSearch.new_index

  # Then we index some documents (a document is a simple Hash with :id and :indexed_field in it)

  idx.index(id: 1, indexed_field: 'red duck')
  idx.index(id: 2, indexed_field: 'yellow big dog')
  idx.index(id: 3, indexed_field: 'small cat')
  idx.index(id: 4, indexed_field: 'red monkey noisy')
  idx.index(id: 5, indexed_field: 'small horse')
  idx.index(id: 6, indexed_field: 'purple turtle')
  idx.index(id: 7, indexed_field: 'tiny red spider')
  idx.index(id: 8, indexed_field: 'big blue whale')
  idx.index(id: 9, indexed_field: 'huge elephant')
  idx.index(id: 10, indexed_field: 'red big cat')

  # Then we can search for our documents

  result = idx.search('RED  cat ')

  # The result will be something like:

  puts result

  # {
  #   documents: [
  #     { document: { id: 10, indexed_field: 'red big cat' }, score: 2.726770362793935 },
  #     { document: { id: 3, indexed_field: 'small cat' }, score: 1.860138656065616 },
  #     { document: { id: 4, indexed_field: 'red monkey noisy' }, score: 0.630035123281377 },
  #     { document: { id: 7, indexed_field: 'tiny red spider' }, score: 0.630035123281377 },
  #     { document: { id: 1, indexed_field: 'red duck' }, score: 0.5589416657904823 }
  #   ],
  #   idfs: {
  #     'cat' => 1.2237754316221157,
  #     'red' => 0.36772478012531734
  #   },
  #   processed_terms: ['red', 'cat']
  # }

We can see results are sorted by score, notice that the document we index can have any other fields like name, price and etc. But only :id and :indexed_field are required

Language support (stop words, stemmers)

Creating an index using MiniSearch.new_index will gives an inverted_index that does not have any language support like stop_words and synonyms. We could pass them as arguments in new_index like:

index = MiniSearch.new_index(
  stop_words: stop_words,
  stemmer: stemmer,
  synonyms_map: synonyms_map
)

Arguments:

The stop_words is a array of worlds that should be removed when indexing the document.
The stemmer is a object of type Stemmer, that implements a stem method that remove all but the stem of the word (example: carrocha -> carr).
The synonyms_map is a hashmap with original terms and a list of synonyms (example: {'calçado' => ['sapato', 'tenis', 'salto', 'chinelo]})

n-gram Tokenizer

By default creating an index using MiniSearch.new_index will gives an inverted_index that uses a simple whitespace tokenizer. (e.g. "Hello World" => ["Hello", "World"])

You can change this behavior to use an n-gram tokenizer which will break words down into smaller pieces with a configurable token window. You can read more about how this kind of tokenization works for Elastic Search. (e.g. ngrams: 2 the phrase "Hello World" => ["He", "el", "ll", "lo", "o ", " W", "Wo", "or", "rl", "ld"]) or (e.g. ngrams: 3 the phrase "Hello World" => ["Hel", "ell", "llo", "lo ", "o W", " Wo", "Wor", "orl", "rld"])

To enable this simply pass an integer for the parameter ngrams.

index = MiniSearch.new_index(
  ngrams: 2,
)

Arguments:

ngrams: An integer which represents the amount of characters each token should be. Common paramaeters are: (2 for bigrams or 3 for trigrams)

Stemmers

Stemmers are classes that implements the def stem(word) method, that receives a word and returs the stem:

Example of a NaiveEnglishStemmer:

module MiniSearch
  module Stemmer
    class NaiveEnglishStemmer
      def stem(word)
        # removes plural
        word[0..-2] if word.end_with?('s')
      end
    end
  end
end

MiniSearch comes with a Brazilian Portuguese stemmer for now.

Configuring multiple cores using yaml

You can configure a multiple core using a yaml config file.

cores:
  - main:
    lang: 'pt'
    synonyms_map:
      bebe: 'nene'
    stop_words:
      - 'de'
      - 'para'
  - aux:
    lang: 'pt'
    synonyms_map:
      bebe: 'nene'
    stop_words:
      - 'de'
      - 'para'

Development

After checking out the repo, run bin/setup to install dependencies. Then, run rake spec to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.

To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to rubygems.org.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/[USERNAME]/mini_search. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.

License

The gem is available as open source under the terms of the MIT License.

Code of Conduct

Everyone interacting in the MiniSearch project’s codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.

andrewaguiar / mini_search