module L::Embeddings

def handle_v(method_name)
  settings = @next_v_settings
  @next_v_settings = nil
  # Unbind the original method
  original_method = instance_method(method_name)
  # Redefine the method
  define_method(method_name) do |*args, &block|
    # # Initialize chunking settings
    # @chunk_by_size = nil
    # # Make `chunk_by_size` method available within the instance
    # define_singleton_method(:chunk_by_size) do |size|
    #   @chunk_by_size = size
    # end
    # Call the original method to get the input
    input = original_method.bind(self).call(*args, &block)
    # # Tokenize the input
    # if @chunk_by_size.blank?
    #   input = [ input ]
    # else
    #   tokenizer = Tokenizers.from_pretrained("bert-base-cased")
    #   input = tokenizer.encode(input).tokens
    # end
    input = [input]
    case settings[:model]
    when *OpenAI::EMBEDDINGS
      client = OpenAI.new(settings)
      client.embeddings(input)
    else
      raise "Unsupported model: #{settings[:model]}"
    end
  end
end

def v(**kwargs)
  @next_v_settings = kwargs
end