Wednesday, November 11, 2009

Build simple PDF search engine in Ruby (Part 1)

I decided to build a simple Ruby search engine to search through PDFs.

The main application was that I wanted a quick way to search through songsheets on my church's Web site. I didn't want to repeatedly look through different PDFs to find the song I was interested in.

I was mostly inspired by this example of someone who had written a search engine in 200 lines of Ruby. I knew my program would be much easier because it didn't need to support any crawling; just indexing and querying.

The first challenge was to find a Ruby library that would parse PDFs. I ultimately settled on this because it was easy to work with. It's basically just a Ruby wrapper around pdftohtml that provides high level access to the text objects of a PDF. I don't care about layout, graphics, etc. so this was sufficient.

The PDF code mostly works without problems but it assumes that the directory for pdftohtml exists in $PATH. I used MacPorts to compile pdftohtml so it was stored in /opt/local/bin, and TextMate didn't recognize /opt/local/bin in my $PATH. I did some research and discovered this page that says I need to create a file called ~/.MacOSX/environment.plist and explicitly set the PATH variable:

  PATH = "/opt/local/bin:/opt/local/sbin:/opt/local/bin:/opt/local/sbin:/opt/local/bin:/opt/local/sbin:/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin:/usr/X11/bin";

The actual indexing code is straightforward. It's mostly based on the saush engine article. Rather than rehash the site, the index is based on an inverted index. The search engine saves the inverted index in a SQLite database using the DataMapper library.

There are three main "tables": Song, Word, and Location. Song and Word have a many-to-many relationship, where a song has multiple words and a word is used in multiple songs. Location is the mapping table between Song and Word.

Here is the indexing library. Note that it uses DataMapper so it relies on the dm-core and dm-timestamps libraries, as well as stemmer and pdf-struct (the PDF library mentioned earlier). The saush search engine uses dm-more but I couldn't get this to be properly included. But dm-timestamps was all that was needed out of dm-more.

Here is the code for index.rb:

require 'rubygems'
require 'dm-core'
require 'dm-timestamps'
require 'dm-aggregates'
require 'stemmer'
require 'pdf-struct'

DBLOC = 'songdb.sqlite3'

DataMapper.setup(:default, 'sqlite3:///' + DBLOC)

class String
 def words
   words = self.gsub(/[^0-9A-Za-z_\s]/,"").split  # self is the string; no need for parms
   # Get rid of all non-word and non-space characters and split on spaces
   d = []
   words.each { |word| d << word.downcase.stem unless word =~ /^[A-G]+[bgm]?$/ } # Ignore guitar chords
   return d

class Song
  include DataMapper::Resource
  property :id,          Serial
  property :title,       String, :length => 255
  has n, :locations
  has n, :words, :through => :locations
  property :created_at,   DateTime
  property :updated_at,   DateTime
  def self.find(title)
    song = first(:title => title)
    song = new(:title => title) if song.nil?
    return song
  def refresh
    update( {:updated_at => DateTime.parse(})

class Word
  include DataMapper::Resource
  property :id,           Serial
  property :stem,         String
  has n, :locations
  has n, :songs, :through => :locations
  def self.find(word)
    wrd = first(:stem => word)
    wrd = new(:stem => word) if wrd.nil?
    return wrd

class Location
  include DataMapper::Resource
  property :id,           Serial
  property :position,     Integer
  belongs_to :word
  belongs_to :song

DataMapper.auto_migrate! if ARGV[0] == 'reset' # This issues the necessary Create statements and wipes out existing database

The actual indexing code goes through each PDF. It extracts the words from the song (except the guitar chords) and creates a space-delimited string of words. Then it goes through the string, creating the Word or Song objects if necessary and creating the many-to-many relationship between Word and Song.

Code for pdfindex.rb:


require 'rubygems'
require 'fileutils'
require 'logger'
require 'index'

SONGDIR = '/Users/rpark/ruby/pdfsearch/'
LOGFILE = 'songsearch.log'
LASTRUN = 'lastrun'

class SongSearch
  def process(file)   # returns string of all stemmed words in song
    array = []
    document =
    document.elements.each do |element|
      array << element.content
    return array.join(" ").words # .join creates a string separated by delimiter
  rescue => e
    #puts "Exception in parsing #{e}"
    @log.debug "Exception in parsing #{e}"

  def index(words, filename)
    if words.nil?
      #puts "ERROR parsing #{filename}"
      @log.debug "ERROR parsing #{filename}"
    print "Indexing #{filename}: "
    logmsg = "Indexing #{filename}: "
    song = Song.find(filename)
      print "Overwriting... "
      logmsg += "Overwriting... "
    words.each_with_index { |word, index|
      loc = => index)
      loc.word, = Word.find(word), song
    puts "#{words.size.to_i} words"
    @log.debug logmsg + "#{words.size.to_i} words"

  def cycle
    lastrun = File.mtime(LASTRUN)
    @log =, 'monthly')
    Dir.glob(SONGDIR + "*.pdf") {
      index(process(file), file) if File.mtime(file) > lastrun  # Only process newer songs
    FileUtils.touch LASTRUN

search =

The digger code actually searches through the song database and searches for songs. A song is searched for by passing a string to It returns a list of songs that the string can be found in, along with a score.

Code for digger.rb:


require 'index'

class Digger
  def search(for_text)
    @search_params = for_text.words
    wrds = []
    @search_params.each { |param| wrds << "stem = '#{param}'" }
    word_sql = "select * from words where #{wrds.join(" or ")}"
    @search_words = repository(:default).adapter.query(word_sql)
    tables, joins, ids = [], [], []
    @search_words.each_with_index { |w, index|
      tables << "locations loc#{index}"
      joins << "loc#{index}.song_id = loc#{index+1}.song_id"
      ids << "loc#{index}.word_id = #{}"
    @common_select = "from #{tables.join(', ')} where #{(joins + ids).join(' and ')} group by loc0.song_id"
  def rank
    merge_rankings(frequency_ranking, location_ranking, distance_ranking)
  def merge_rankings(*rankings)
    r = {}
    rankings.each { |ranking| r.merge!(ranking) { |key, oldval, newval| oldval + newval} }
    r.sort {|a,b| b[1] <=> a[1]}
  def frequency_ranking
    freq_sql= "select loc0.song_id, count(loc0.song_id) as count #{@common_select} order by count desc"
    list = repository(:default).adapter.query(freq_sql)
    rank = {}
    list.size.times { |i| rank[list[i].song_id] = list[i].count.to_f/list[0].count.to_f }
#puts freq_sql
#puts list
#puts rank.inspect
    return rank
  def location_ranking
    total = []
    @search_words.each_with_index { |w, index| total << "loc#{index}.position + 1" }
    loc_sql = "select loc0.song_id, (#{total.join(' + ')}) as total #{@common_select} order by total asc"
    list = repository(:default).adapter.query(loc_sql)
    rank = {}
    list.size.times { |i| rank[list[i].song_id] = list[0].total.to_f/list[i].total.to_f }
#puts loc_sql
#puts list
#puts rank.inspect
    return rank

  def distance_ranking
    return {} if @search_words.size == 1
    dist, total = [], []
    @search_words.each_with_index { |w, index| total << "loc#{index}.position" }
    total.size.times { |index| dist << "abs(#{total[index]} - #{total[index + 1]})" unless index == total.size - 1 }
    dist_sql = "select loc0.song_id, (#{dist.join(' + ')}) as dist #{@common_select} order by dist asc"
    list = repository(:default).adapter.query(dist_sql)
    rank =
    list.size.times { |i| rank[list[i].song_id] = list[0].dist.to_f/list[i].dist.to_f }
#puts dist_sql
#puts list
#puts rank.inspect
    return rank

Note: the biggest disadvantage with this search method is that it doesn't show the search string in its context in the song. Rather than continue with this approach, my thinking is to use a search engine such as Solr to do the search, so I can show the search string within the song.


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