Project

ruby-spacy

0.02
The project is in a healthy, maintained state
ruby-spacy is a wrapper module for using spaCy from the Ruby programming language via PyCall. This module aims to make it easy and natural for Ruby programmers to use spaCy. This module covers the areas of spaCy functionality for using many varieties of its language models, not for building ones.
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 Dependencies

Development

Runtime

~> 0.4.0
~> 1.5.1
 Project Readme

💎 ruby-spacy

Overview

ruby-spacy is a wrapper module for using spaCy from the Ruby programming language via PyCall. This module aims to make it easy and natural for Ruby programmers to use spaCy. This module covers the areas of spaCy functionality for using many varieties of its language models, not for building ones.

Functionality
Tokenization, lemmatization, sentence segmentation
Part-of-speech tagging and dependency parsing
Named entity recognition
Syntactic dependency visualization
Access to pre-trained word vectors
OpenAI Chat/Completion/Embeddings API integration

Current Version: 0.2.3

  • spaCy 3.7.0 supported
  • OpenAI API integration

Installation of Prerequisites

IMPORTANT: Make sure that the enable-shared option is enabled in your Python installation. You can use pyenv to install any version of Python you like. Install Python 3.10.6, for instance, using pyenv with enable-shared as follows:

$ env CONFIGURE_OPTS="--enable-shared" pyenv install 3.10.6

Remember to make it accessible from your working directory. It is recommended that you set global to the version of python you just installed.

$ pyenv global 3.10.6 

Then, install spaCy. If you use pip, the following command will do:

$ pip install spacy

Install trained language models. For a starter, en_core_web_sm will be the most useful to conduct basic text processing in English. However, if you want to use advanced features of spaCy, such as named entity recognition or document similarity calculation, you should also install a larger model like en_core_web_lg.

$ python -m spacy download en_core_web_sm
$ python -m spacy download en_core_web_lg

See Spacy: Models & Languages for other models in various languages. To install models for the Japanese language, for instance, you can do it as follows:

$ python -m spacy download ja_core_news_sm
$ python -m spacy download ja_core_news_lg

Installation of ruby-spacy

Add this line to your application's Gemfile:

gem 'ruby-spacy'

And then execute:

$ bundle install

Or install it yourself as:

$ gem install ruby-spacy

Usage

See Examples below.

Examples

Many of the following examples are Python-to-Ruby translations of code snippets in spaCy 101. For more examples, look inside the examples directory.

Tokenization

spaCy: Tokenization

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("en_core_web_sm")

doc = nlp.read("Apple is looking at buying U.K. startup for $1 billion")

row = []

doc.each do |token|
  row << token.text
end

headings = [1,2,3,4,5,6,7,8,9,10]
table = Terminal::Table.new rows: [row], headings: headings

puts table

Output:

1 2 3 4 5 6 7 8 9 10 11
Apple is looking at buying U.K. startup for $ 1 billion

Part-of-speech and Dependency

spaCy: Part-of-speech tags and dependencies

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("Apple is looking at buying U.K. startup for $1 billion")

headings = ["text", "lemma", "pos", "tag", "dep"]
rows = []

doc.each do |token|
  rows << [token.text, token.lemma, token.pos, token.tag, token.dep]
end

table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

text lemma pos tag dep
Apple Apple PROPN NNP nsubj
is be AUX VBZ aux
looking look VERB VBG ROOT
at at ADP IN prep
buying buy VERB VBG pcomp
U.K. U.K. PROPN NNP dobj
startup startup NOUN NN advcl
for for ADP IN prep
$ $ SYM $ quantmod
1 1 NUM CD compound
billion billion NUM CD pobj

Part-of-speech and Dependency (Japanese)

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("ja_core_news_lg")
doc = nlp.read("任天堂は1983年にファミコンを14,800円で発売した。")

headings = ["text", "lemma", "pos", "tag", "dep"]
rows = []

doc.each do |token|
  rows << [token.text, token.lemma, token.pos, token.tag, token.dep]
end

table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

text lemma pos tag dep
任天堂 任天堂 PROPN 名詞-固有名詞-一般 nsubj
ADP 助詞-係助詞 case
1983 1983 NUM 名詞-数詞 nummod
NOUN 名詞-普通名詞-助数詞可能 obl
ADP 助詞-格助詞 case
ファミコン ファミコン NOUN 名詞-普通名詞-一般 obj
ADP 助詞-格助詞 case
14,800 14,800 NUM 名詞-数詞 fixed
NOUN 名詞-普通名詞-助数詞可能 obl
ADP 助詞-格助詞 case
発売 発売 VERB 名詞-普通名詞-サ変可能 ROOT
する AUX 動詞-非自立可能 aux
AUX 助動詞 aux
PUNCT 補助記号-句点 punct

Morphology

POS and morphology tags

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("Apple is looking at buying U.K. startup for $1 billion")

headings = ["text", "shape", "is_alpha", "is_stop", "morphology"]
rows = []

doc.each do |token|
  morph = token.morphology.map do |k, v|
    "#{k} = #{v}"
  end.join("\n")
  rows << [token.text, token.shape, token.is_alpha, token.is_stop, morph]
end

table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

text shape is_alpha is_stop morphology
Apple Xxxxx true false NounType = Prop
Number = Sing
is xx true true Mood = Ind
Number = Sing
Person = 3
Tense = Pres
VerbForm = Fin
looking xxxx true false Aspect = Prog
Tense = Pres
VerbForm = Part
at xx true true
buying xxxx true false Aspect = Prog
Tense = Pres
VerbForm = Part
U.K. X.X. false false NounType = Prop
Number = Sing
startup xxxx true false Number = Sing
for xxx true true
$ $ false false
1 d false false NumType = Card
billion xxxx true false NumType = Card

Visualizing Dependency

spaCy: Visualizers

Ruby code:

require "ruby-spacy"

nlp = Spacy::Language.new("en_core_web_sm")

sentence = "Autonomous cars shift insurance liability toward manufacturers"
doc = nlp.read(sentence)

dep_svg = doc.displacy(style: "dep", compact: false)

File.open(File.join("test_dep.svg"), "w") do |file|
  file.write(dep_svg)
end

Output:

Visualizing Dependency (Compact)

Ruby code:

require "ruby-spacy"

nlp = Spacy::Language.new("en_core_web_sm")

sentence = "Autonomous cars shift insurance liability toward manufacturers"
doc = nlp.read(sentence)

dep_svg = doc.displacy(style: "dep", compact: true)

File.open(File.join("test_dep_compact.svg"), "w") do |file|
  file.write(dep_svg)
end

Output:

Named Entity Recognition

spaCy: Named entities

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("en_core_web_sm")
doc =nlp.read("Apple is looking at buying U.K. startup for $1 billion")

rows = []

doc.ents.each do |ent|
  rows << [ent.text, ent.start_char, ent.end_char, ent.label]
end

headings = ["text", "start_char", "end_char", "label"]
table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

text start_char end_char label
Apple 0 5 ORG
U.K. 27 31 GPE
$1 billion 44 54 MONEY

Named Entity Recognition (Japanese)

Ruby code:

require( "ruby-spacy")
require "terminal-table"

nlp = Spacy::Language.new("ja_core_news_lg")

sentence = "任天堂は1983年にファミコンを14,800円で発売した。"
doc = nlp.read(sentence)

rows = []

doc.ents.each do |ent|
  rows << [ent.text, ent.start_char, ent.end_char, ent.label]
end

headings = ["text", "start", "end", "label"]
table = Terminal::Table.new rows: rows, headings: headings
print table

Output:

text start end label
任天堂 0 3 ORG
1983年 4 9 DATE
ファミコン 10 15 PRODUCT
14,800円 16 23 MONEY

Checking Availability of Word Vectors

spaCy: Word vectors and similarity

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("en_core_web_lg")
doc = nlp.read("dog cat banana afskfsd")

rows = []

doc.each do |token|
  rows << [token.text, token.has_vector, token.vector_norm, token.is_oov]
end

headings = ["text", "has_vector", "vector_norm", "is_oov"]
table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

text has_vector vector_norm is_oov
dog true 7.0336733 false
cat true 6.6808186 false
banana true 6.700014 false
afskfsd false 0.0 true

Similarity Calculation

Ruby code:

require "ruby-spacy"

nlp = Spacy::Language.new("en_core_web_lg")
doc1 = nlp.read("I like salty fries and hamburgers.")
doc2 = nlp.read("Fast food tastes very good.")

puts "Doc 1: " + doc1.text
puts "Doc 2: " + doc2.text
puts "Similarity: #{doc1.similarity(doc2)}"

Output:

Doc 1: I like salty fries and hamburgers.
Doc 2: Fast food tastes very good.
Similarity: 0.7687607012190486

Similarity Calculation (Japanese)

Ruby code:

require "ruby-spacy"

nlp = Spacy::Language.new("ja_core_news_lg")
ja_doc1 = nlp.read("今日は雨ばっかり降って、嫌な天気ですね。")
puts "doc1: #{ja_doc1.text}"
ja_doc2 = nlp.read("あいにくの悪天候で残念です。")
puts "doc2: #{ja_doc2.text}"
puts "Similarity: #{ja_doc1.similarity(ja_doc2)}"

Output:

doc1: 今日は雨ばっかり降って、嫌な天気ですね。
doc2: あいにくの悪天候で残念です。
Similarity: 0.8684192637149641

Word Vector Calculation

Tokyo - Japan + France = Paris ?

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("en_core_web_lg")

tokyo = nlp.get_lexeme("Tokyo")
japan = nlp.get_lexeme("Japan")
france = nlp.get_lexeme("France")

query = tokyo.vector - japan.vector + france.vector

headings = ["rank", "text", "score"]
rows = []

results = nlp.most_similar(query, 10)
results.each_with_index do |lexeme, i|
  index = (i + 1).to_s
  rows << [index, lexeme.text, lexeme.score]
end

table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

rank text score
1 FRANCE 0.8346999883651733
2 France 0.8346999883651733
3 france 0.8346999883651733
4 PARIS 0.7703999876976013
5 paris 0.7703999876976013
6 Paris 0.7703999876976013
7 TOULOUSE 0.6381999850273132
8 Toulouse 0.6381999850273132
9 toulouse 0.6381999850273132
10 marseille 0.6370999813079834

Word Vector Calculation (Japanese)

東京 - 日本 + フランス = パリ ?

Ruby code:

require "ruby-spacy"
require "terminal-table"

nlp = Spacy::Language.new("ja_core_news_lg")

tokyo = nlp.get_lexeme("東京")
japan = nlp.get_lexeme("日本")
france = nlp.get_lexeme("フランス")

query = tokyo.vector - japan.vector + france.vector

headings = ["rank", "text", "score"]
rows = []

results = nlp.most_similar(query, 10)
results.each_with_index do |lexeme, i|
  index = (i + 1).to_s
  rows << [index, lexeme.text, lexeme.score]
end

table = Terminal::Table.new rows: rows, headings: headings
puts table

Output:

rank text score
1 パリ 0.7376999855041504
2 フランス 0.7221999764442444
3 東京 0.6697999835014343
4 ストラスブール 0.631600022315979
5 リヨン 0.5939000248908997
6 Paris 0.574400007724762
7 ベルギー 0.5683000087738037
8 ニース 0.5679000020027161
9 アルザス 0.5644999742507935
10 南仏 0.5547999739646912

OpenAI API Integration

⚠️ This feature is currently experimental. Details are subject to change. Please refer to OpenAI's API reference and Ruby OpenAI for available parameters (max_tokens, temperature, etc).

Easily leverage GPT models within ruby-spacy by using an OpenAI API key. When constructing prompts for the Doc::openai_query method, you can incorporate the following token properties of the document. These properties are retrieved through function calls (made internally by GPT when necessary) and seamlessly integrated into your prompt. Note that function calls need gpt-4o-mini or greater. The available properties include:

  • surface
  • lemma
  • tag
  • pos (part of speech)
  • dep (dependency)
  • ent_type (entity type)
  • morphology

GPT Prompting (Translation)

Ruby code:

require "ruby-spacy"

api_key = ENV["OPENAI_API_KEY"]
nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("The Beatles released 12 studio albums")

# default parameter values
# max_tokens: 1000
# temperature: 0.7
# model: "gpt-4o-mini"
res1 = doc.openai_query(
  access_token: api_key,
  prompt: "Translate the text to Japanese."
)
puts res1

Output:

ビートルズは12枚のスタジオアルバムをリリースしました。

GPT Prompting (Elaboration)

Ruby code:

require "ruby-spacy"

api_key = ENV["OPENAI_API_KEY"]
nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("The Beatles were an English rock band formed in Liverpool in 1960.")

# default parameter values
# max_tokens: 1000
# temperature: 0.7
# model: "gpt-4o-mini"
res = doc.openai_query(
  access_token: api_key,
  prompt: "Extract the topic of the document and list 10 entities (names, concepts, locations, etc.) that are relevant to the topic."
)

Output:

Topic: The Beatles

Relevant Entities:

  1. The Beatles (PERSON)
  2. Liverpool (GPE - Geopolitical Entity)
  3. English (LANGUAGE)
  4. Rock (MUSIC GENRE)
  5. 1960 (DATE)
  6. Band (MUSIC GROUP)
  7. John Lennon (PERSON - key member)
  8. Paul McCartney (PERSON - key member)
  9. George Harrison (PERSON - key member)
  10. Ringo Starr (PERSON - key member)

GPT Prompting (JSON Output Using RAG with Token Properties)

Ruby code:

require "ruby-spacy"

api_key = ENV["OPENAI_API_KEY"]
nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("The Beatles released 12 studio albums")

# default parameter values
# max_tokens: 1000
# temperature: 0.7
# model: "gpt-4o-mini"
res = doc.openai_query(
  access_token: api_key,
  prompt: "List token data of each of the words used in the sentence. Add 'meaning' property and value (brief semantic definition) to each token data. Output as a JSON object."
)

Output:

{
  "tokens": [
    {
      "surface": "The",
      "lemma": "the",
      "pos": "DET",
      "tag": "DT",
      "dep": "det",
      "ent_type": "",
      "morphology": "{'Definite': 'Def', 'PronType': 'Art'}",
      "meaning": "A definite article used to specify a noun."
    },
    {
      "surface": "Beatles",
      "lemma": "beatle",
      "pos": "NOUN",
      "tag": "NNS",
      "dep": "nsubj",
      "ent_type": "GPE",
      "morphology": "{'Number': 'Plur'}",
      "meaning": "A British rock band formed in Liverpool in 1960."
    },
    {
      "surface": "released",
      "lemma": "release",
      "pos": "VERB",
      "tag": "VBD",
      "dep": "ROOT",
      "ent_type": "",
      "morphology": "{'Tense': 'Past', 'VerbForm': 'Fin'}",
      "meaning": "To make something available to the public."
    },
    {
      "surface": "12",
      "lemma": "12",
      "pos": "NUM",
      "tag": "CD",
      "dep": "nummod",
      "ent_type": "CARDINAL",
      "morphology": "{'NumType': 'Card'}",
      "meaning": "A cardinal number representing the quantity of twelve."
    },
    {
      "surface": "studio",
      "lemma": "studio",
      "pos": "NOUN",
      "tag": "NN",
      "dep": "compound",
      "ent_type": "",
      "morphology": "{'Number': 'Sing'}",
      "meaning": "A place where recording or filming takes place."
    },
    {
      "surface": "albums",
      "lemma": "album",
      "pos": "NOUN",
      "tag": "NNS",
      "dep": "dobj",
      "ent_type": "",
      "morphology": "{'Number': 'Plur'}",
      "meaning": "Collections of music tracks or recordings."
    }
  ]
}

GPT Prompting (Generate a Syntaxt Tree using Token Properties)

Ruby code:

require "ruby-spacy"

api_key = ENV["OPENAI_API_KEY"]
nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("The Beatles released 12 studio albums")

# default parameter values
# max_tokens: 1000
# temperature: 0.7
res = doc.openai_query(
  access_token: api_key,
  model: "gpt-4",
  prompt: "Generate a tree diagram from the text using given token data. Use the following bracketing style: [S [NP [Det the] [N cat]] [VP [V sat] [PP [P on] [NP the mat]]]"
)
puts res

Output:

[S
  [NP
    [Det The]
    [N Beatles]
  ]
  [VP
    [V released]
    [NP
      [Num 12]
      [N
        [N studio]
        [N albums]
      ]
    ]
  ]
]

GPT Text Completion

Ruby code:

require "ruby-spacy"

api_key = ENV["OPENAI_API_KEY"]
nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("Vladimir Nabokov was a")

# default parameter values
# max_tokens: 1000
# temperature: 0.7
# model: "gpt-4o-mini"
res = doc.openai_completion(access_token: api_key)
puts res

Output:

Vladimir Nabokov was a Russian-American novelist, poet, and entomologist, best known for his intricate prose style and innovative narrative techniques. He is most famously recognized for his controversial novel "Lolita," which explores themes of obsession and manipulation. Nabokov's works often reflect his fascination with language, memory, and the nature of art. In addition to his literary accomplishments, he was also a passionate lepidopterist, contributing to the field of butterfly studies. His literary career spanned several decades, and his influence continues to be felt in contemporary literature.

Text Embeddings

Ruby code:

require "ruby-spacy"

api_key = ENV["OPENAI_API_KEY"]
nlp = Spacy::Language.new("en_core_web_sm")
doc = nlp.read("Vladimir Nabokov was a Russian-American novelist, poet, translator and entomologist.")

# default model: text-embedding-ada-002
res = doc.openai_embeddings(access_token: api_key)

puts res

Output:

-0.0023891362
-0.016671216
0.010879759
0.012918914
0.0012281279
...

Advanced Usage

Setting a Timeout

You can set a timeout for the Spacy::Language.new method:

nlp = Spacy::Language.new("en_core_web_sm", timeout: 120) # Set timeout to 120 seconds

Author

Yoichiro Hasebe [yohasebe@gmail.com]

Acknowlegments

I would like to thank the following open source projects and their creators for making this project possible:

License

This library is available as open source under the terms of the MIT License.