docs: def add_embedding_word2vec(self, x, path, dimensions=None, header_with_dimensions=True):
          """
          Parse the word2vec file and extracts vectors as bytes and labels as TSV file.
          The format is simple: It's a UTF-8 encoded file, each word + vectors separated by new line.
          Vector is space separated.
          At the very first line might be dimensions, given as space separated value.
  
  
          Line 1: 2 4\n
          Line 2: word 200.3 4004.4 34.2 22.3\n
          Line 3: word2 20.0 4.4 4.2 0.022\n
          and so on
  
          For performance reasons, you should prefer add_embedding_path().
  
          """
          if path.endswith('.txt'):
              if not os.path.exists(path):
                  raise Exception("Given word2vec file does not exist: " + path)
  
              f = open(path, 'r')
  
              if not header_with_dimensions and not dimensions:
                  raise Exception('Either the word2vec file should contain the dimensions as header or it needs to be'
                                  'specified manually using dimensions=[x,y] argument.')
  
              if header_with_dimensions:
                  line = f.readline()
                  if ' ' not in line:
                      raise Exception('Given word2vec file should have in first line the dimensions, e.g.: 1000 200')
                  dimensions = np.fromstring(line, dtype=np.uint, sep=' ').tolist()
  
              labels = ''
              vectors = ''
              line_pos = 1 if header_with_dimensions else 0
  
              if len(dimensions) != 2:
                  raise Exception('dimensions invalid shape. e.g. [200, 32] => 200 rows, 32 cols.')
  
              for line in iter(f.readline, ''):
                  line_pos += 1
                  space_pos = line.find(' ')
                  if -1 == space_pos:
                      message = 'Given word2vec does not have correct format in line ' + str(line_pos)
                      message += '\nGot: ' + str(line)
                      raise Exception(message)
  
                  labels += line[:space_pos] + '\n'
                  vectors += line[space_pos+1:] + ' '
  
              vectors = np.fromstring(vectors, dtype=np.float32, sep=' ').tobytes()
          else:
              raise Exception("Given word2vec is not a .txt file. Other file formats are not supported.")
  
          info = {
              'dimensions': dimensions
          }
  
          name = os.path.basename(path)
          self._ensure_insight(x)
          remote_path = 'aetros/job/insight/'+str(x)+'/embedding/'
          with self.git.batch_commit('INSIGHT_EMBEDDING ' + str(x)):
              self.git.commit_file('WORD2VEC', remote_path + name + '/tensor.bytes', vectors)
              self.git.commit_file('WORD2VEC', remote_path + name + '/metadata.tsv', labels)
              self.git.commit_file('WORD2VEC', remote_path + name + '/info.json', simplejson.dumps(info))