Class: Spacy::Doc

Inherits:
Object
  • Object
show all
Includes:
Enumerable
Defined in:
lib/ruby-spacy.rb

Overview

See also spaCy Python API document for [‘Doc`](spacy.io/api/doc).

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(nlp, py_doc: nil, text: nil, max_retrial: MAX_RETRIAL, retrial: 0) ⇒ Doc

It is recommended to use Language#read method to create a doc. If you need to create one using #initialize, there are two method signatures: ‘Spacy::Doc.new(nlp_id, py_doc: Object)` and `Spacy::Doc.new(nlp_id, text: String)`.

Parameters:

  • nlp (Language) —

    an instance of Language class

  • py_doc (Object) (defaults to: nil) —

    an instance of Python ‘Doc` class

  • text (String) (defaults to: nil) —

    the text string to be analyzed



87
88
89
90
91
92
93
94
95
96
97
98
 
# File 'lib/ruby-spacy.rb', line 87

def initialize(nlp, py_doc: nil, text: nil, max_retrial: MAX_RETRIAL,
               retrial: 0)
  @py_nlp = nlp
  @py_doc = py_doc || @py_doc = nlp.call(text)
  @text = @py_doc.text
rescue StandardError
  retrial += 1
  raise "Error: Failed to construct a Doc object" unless retrial <= max_retrial

  sleep 0.5
  initialize(nlp, py_doc: py_doc, text: text, max_retrial: max_retrial, retrial: retrial)
end

Dynamic Method Handling

This class handles dynamic methods through the method_missing method

#method_missing(name, *args) ⇒ Object

Methods defined in Python but not wrapped in ruby-spacy can be called by this dynamic method handling mechanism.



350
351
352
 
# File 'lib/ruby-spacy.rb', line 350

def method_missing(name, *args)
  @py_doc.send(name, *args)
end

Instance Attribute Details

#py_doc ⇒ Object (readonly)

Returns a Python ‘Doc` instance accessible via `PyCall`.

Returns:

  • (Object) —

    a Python ‘Doc` instance accessible via `PyCall`



70
71
72
 
# File 'lib/ruby-spacy.rb', line 70

def py_doc
  @py_doc
end

#py_nlp ⇒ Object (readonly)

Returns a Python ‘Language` instance accessible via `PyCall`.

Returns:

  • (Object) —

    a Python ‘Language` instance accessible via `PyCall`



67
68
69
 
# File 'lib/ruby-spacy.rb', line 67

def py_nlp
  @py_nlp
end

#text ⇒ String (readonly)

Returns a text string of the document.

Returns:

  • (String) —

    a text string of the document



73
74
75
 
# File 'lib/ruby-spacy.rb', line 73

def text
  @text
end

Instance Method Details

#[](range) ⇒ Object

Returns a span if given a range object; or returns a token if given an integer representing a position in the doc.

Parameters:

  • range (Range) —

    an ordinary Ruby’s range object such as ‘0..3`, `1…4`, or `3 .. -1`



203
204
205
206
207
208
209
210
 
# File 'lib/ruby-spacy.rb', line 203

def [](range)
  if range.is_a?(Range)
    py_span = @py_doc[range]
    Span.new(self, start_index: py_span.start, end_index: py_span.end - 1)
  else
    Token.new(@py_doc[range])
  end
end

#displacy(style: "dep", compact: false) ⇒ String

Visualize the document in one of two styles: “dep” (dependencies) or “ent” (named entities).

Parameters:

  • style (String) (defaults to: "dep") —

    either ‘dep` or `ent`

  • compact (Boolean) (defaults to: false) —

    only relevant to the ‘dep’ style

Returns:

  • (String) —

    in the case of ‘dep`, the output text will be an SVG, whereas in the `ent` style, the output text will be an HTML.



223
224
225
 
# File 'lib/ruby-spacy.rb', line 223

def displacy(style: "dep", compact: false)
  PyDisplacy.render(py_doc, style: style, options: { compact: compact }, jupyter: false)
end

#each ⇒ Object

Iterates over the elements in the doc yielding a token instance each time.



139
140
141
142
143
 
# File 'lib/ruby-spacy.rb', line 139

def each
  PyCall::List.call(@py_doc).each do |py_token|
    yield Token.new(py_token)
  end
end

#ents ⇒ Array<Span>

Returns an array of spans each representing a named entity.

Returns:



189
190
191
192
193
194
195
196
197
198
199
 
# File 'lib/ruby-spacy.rb', line 189

def ents
  # so that ents canbe "each"-ed in Ruby
  ent_array = []
  PyCall::List.call(@py_doc.ents).each do |ent|
    ent.define_singleton_method :label do
      label_
    end
    ent_array << ent
  end
  ent_array
end

#noun_chunks ⇒ Array<Span>

Returns an array of spans representing noun chunks.

Returns:



167
168
169
170
171
172
173
174
 
# File 'lib/ruby-spacy.rb', line 167

def noun_chunks
  chunk_array = []
  py_chunks = PyCall::List.call(@py_doc.noun_chunks)
  py_chunks.each do |py_chunk|
    chunk_array << Span.new(self, start_index: py_chunk.start, end_index: py_chunk.end - 1)
  end
  chunk_array
end

#openai_completion(access_token: nil, max_tokens: 1000, temperature: 0.7, model: "gpt-4o-mini") ⇒ Object 



305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
 
# File 'lib/ruby-spacy.rb', line 305

def openai_completion(access_token: nil, max_tokens: 1000, temperature: 0.7, model: "gpt-4o-mini")
  messages = [
    { role: "system", content: "Complete the text input by the user." },
    { role: "user", content: @text }
  ]
  access_token ||= ENV["OPENAI_API_KEY"]
  raise "Error: OPENAI_API_KEY is not set" unless access_token

  begin
    response = Spacy.openai_client(access_token: access_token).chat(
      parameters: {
        model: model,
        messages: messages,
        max_tokens: max_tokens,
        temperature: temperature
      }
    )
    response.dig("choices", 0, "message", "content")
  rescue StandardError => e
    puts "Error: OpenAI API call failed."
    pp e.message
    pp e.backtrace
  end
end

#openai_embeddings(access_token: nil, model: "text-embedding-ada-002") ⇒ Object 



330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
 
# File 'lib/ruby-spacy.rb', line 330

def openai_embeddings(access_token: nil, model: "text-embedding-ada-002")
  access_token ||= ENV["OPENAI_API_KEY"]
  raise "Error: OPENAI_API_KEY is not set" unless access_token

  begin
    response = Spacy.openai_client(access_token: access_token).embeddings(
      parameters: {
        model: model,
        input: @text
      }
    )
    response.dig("data", 0, "embedding")
  rescue StandardError => e
    puts "Error: OpenAI API call failed."
    pp e.message
    pp e.backtrace
  end
end

#openai_query(access_token: nil, max_tokens: 1000, temperature: 0.7, model: "gpt-4o-mini", messages: [], prompt: nil) ⇒ Object 



227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
 
# File 'lib/ruby-spacy.rb', line 227

def openai_query(access_token: nil,
                 max_tokens: 1000,
                 temperature: 0.7,
                 model: "gpt-4o-mini",
                 messages: [],
                 prompt: nil)
  if messages.empty?
    messages = [
      { role: "system", content: prompt },
      { role: "user", content: @text }
    ]
  end

  access_token ||= ENV["OPENAI_API_KEY"]
  raise "Error: OPENAI_API_KEY is not set" unless access_token

  begin
    response = Spacy.openai_client(access_token: access_token).chat(
      parameters: {
        model: model,
        messages: messages,
        max_tokens: max_tokens,
        temperature: temperature,
        function_call: "auto",
        stream: false,
        functions: [
          {
            name: "get_tokens",
            description: "Tokenize given text and return a list of tokens with their attributes: surface, lemma, tag, pos (part-of-speech), dep (dependency), ent_type (entity type), and morphology",
            "parameters": {
              "type": "object",
              "properties": {
                "text": {
                  "type": "string",
                  "description": "text to be tokenized"
                }
              },
              "required": ["text"]
            }
          }
        ]
      }
    )

    message = response.dig("choices", 0, "message")

    if message["role"] == "assistant" && message["function_call"]
      messages << message
      function_name = message.dig("function_call", "name")
      _args = JSON.parse(message.dig("function_call", "arguments"))
      case function_name
      when "get_tokens"
        res = tokens.map do |t|
          {
            "surface": t.text,
            "lemma": t.lemma,
            "pos": t.pos,
            "tag": t.tag,
            "dep": t.dep,
            "ent_type": t.ent_type,
            "morphology": t.morphology
          }
        end.to_json
      end
      messages << { role: "system", content: res }
      openai_query(access_token: access_token, max_tokens: max_tokens,
                   temperature: temperature, model: model,
                   messages: messages, prompt: prompt)
    else
      message["content"]
    end
  rescue StandardError => e
    puts "Error: OpenAI API call failed."
    pp e.message
    pp e.backtrace
  end
end

#respond_to_missing?(sym) ⇒ Boolean

Returns:

  • (Boolean) 


354
355
356
 
# File 'lib/ruby-spacy.rb', line 354

def respond_to_missing?(sym)
  sym ? true : super
end

#retokenize(start_index, end_index, attributes = {}) ⇒ Object

Retokenizes the text merging a span into a single token.

Parameters:

  • start_index (Integer) —

    the start position of the span to be retokenized in the document

  • end_index (Integer) —

    the end position of the span to be retokenized in the document

  • attributes (Hash) (defaults to: {}) —

    attributes to set on the merged token



104
105
106
107
108
 
# File 'lib/ruby-spacy.rb', line 104

def retokenize(start_index, end_index, attributes = {})
  PyCall.with(@py_doc.retokenize) do |retokenizer|
    retokenizer.merge(@py_doc[start_index..end_index], attrs: attributes)
  end
end

#retokenize_split(pos_in_doc, split_array, head_pos_in_split, ancestor_pos, attributes = {}) ⇒ Object

Retokenizes the text splitting the specified token.

Parameters:

  • pos_in_doc (Integer) —

    the position of the span to be retokenized in the document

  • split_array (Array<String>) —

    text strings of the split results

  • ancestor_pos (Integer) —

    the position of the immediate ancestor element of the split elements in the document

  • attributes (Hash) (defaults to: {}) —

    the attributes of the split elements



115
116
117
118
119
120
 
# File 'lib/ruby-spacy.rb', line 115

def retokenize_split(pos_in_doc, split_array, head_pos_in_split, ancestor_pos, attributes = {})
  PyCall.with(@py_doc.retokenize) do |retokenizer|
    heads = [[@py_doc[pos_in_doc], head_pos_in_split], @py_doc[ancestor_pos]]
    retokenizer.split(@py_doc[pos_in_doc], split_array, heads: heads, attrs: attributes)
  end
end

#sents ⇒ Array<Span>

Returns an array of spans each representing a sentence.

Returns:



178
179
180
181
182
183
184
185
 
# File 'lib/ruby-spacy.rb', line 178

def sents
  sentence_array = []
  py_sentences = PyCall::List.call(@py_doc.sents)
  py_sentences.each do |py_sent|
    sentence_array << Span.new(self, start_index: py_sent.start, end_index: py_sent.end - 1)
  end
  sentence_array
end

#similarity(other) ⇒ Float

Returns a semantic similarity estimate.

Parameters:

  • other (Doc) —

    the other doc to which a similarity estimation is made

Returns:

  • (Float) 


215
216
217
 
# File 'lib/ruby-spacy.rb', line 215

def similarity(other)
  py_doc.similarity(other.py_doc)
end

#span(range_or_start, optional_size = nil) ⇒ Span

Returns a span of the specified range within the doc. The method should be used either of the two ways: ‘Doc#span(range)` or `Doc#spansize_of_span`.

Parameters:

  • range_or_start (Range, Integer) —

    a range object, or, alternatively, an integer that represents the start position of the span

  • optional_size (Integer) (defaults to: nil) —

    an integer representing the size of the span

Returns:



150
151
152
153
154
155
156
157
158
159
160
161
162
163
 
# File 'lib/ruby-spacy.rb', line 150

def span(range_or_start, optional_size = nil)
  if optional_size
    start_index = range_or_start
    temp = tokens[start_index...start_index + optional_size]
  else
    start_index = range_or_start.first
    range = range_or_start
    temp = tokens[range]
  end

  end_index = start_index + temp.size - 1

  Span.new(self, start_index: start_index, end_index: end_index)
end

#to_s ⇒ String

String representation of the document.

Returns:

  • (String) 


124
125
126
 
# File 'lib/ruby-spacy.rb', line 124

def to_s
  @text
end

#tokens ⇒ Array<Token>

Returns an array of tokens contained in the doc.

Returns:



130
131
132
133
134
135
136
 
# File 'lib/ruby-spacy.rb', line 130

def tokens
  results = []
  PyCall::List.call(@py_doc).each do |py_token|
    results << Token.new(py_token)
  end
  results
end