Class: SemanticTextChunker::BoundaryDetector

Inherits:
Object
  • Object
show all
Defined in:
lib/semantic_text_chunker/boundary_detector.rb

Instance Method Summary collapse

Constructor Details

#initialize(sentences:, embeddings:, threshold:, max_tokens:, embedder:, forced: []) ⇒ BoundaryDetector

Returns a new instance of BoundaryDetector.



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# File 'lib/semantic_text_chunker/boundary_detector.rb', line 5

def initialize(sentences:, embeddings:, threshold:, max_tokens:, embedder:, forced: [])
  @sentences  = sentences
  @embeddings = embeddings
  @threshold  = threshold
  @max_tokens = max_tokens
  @embedder   = embedder
  @forced     = forced.to_set
end

Instance Method Details

#boundaries ⇒ Object

Returns array of sentence indices where chunks end



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# File 'lib/semantic_text_chunker/boundary_detector.rb', line 15

def boundaries
  return [] if @sentences.size <= 1

  boundaries  = []
  chunk_start = 0

  @sentences.each_with_index do |sentence, i|
    next if i == 0

    # Hard structural boundary: the previous sentence ended a block, so the
    # chunk must end there regardless of similarity or token count.
    if @forced.include?(i - 1)
      boundaries << (i - 1) unless boundaries.last == (i - 1)
      chunk_start = i
      next
    end

    current_text = @sentences[chunk_start..i - 1].join(" ")
    next_text    = current_text + " " + sentence

    # Force boundary if adding this sentence exceeds token limit
    if tokens(next_text) > @max_tokens
      boundaries << i - 1
      chunk_start = i
      next
    end

    # Compute similarity between accumulated chunk and next sentence
    chunk_embedding    = mean_embedding(@embeddings[chunk_start..i - 1])
    sentence_embedding = @embeddings[i]
    similarity         = @embedder.cosine_similarity(chunk_embedding, sentence_embedding)

    if similarity < @threshold
      boundaries << i - 1
      chunk_start = i
    end
  end

  boundaries
end