108 lines
3.3 KiB
Ruby
108 lines
3.3 KiB
Ruby
# frozen_string_literal: true
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module DiscourseAi
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module AiHelper
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class SemanticCategorizer
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def initialize(input, user)
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@user = user
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@text = input[:text]
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end
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def categories
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return [] if @text.blank?
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return [] unless SiteSetting.ai_embeddings_enabled
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candidates = nearest_neighbors(limit: 100)
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candidate_ids = candidates.map(&:first)
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::Topic
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.joins(:category)
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.where(id: candidate_ids)
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.where("categories.id IN (?)", Category.topic_create_allowed(@user.guardian).pluck(:id))
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.order("array_position(ARRAY#{candidate_ids}, topics.id)")
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.pluck(
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"categories.id",
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"categories.name",
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"categories.slug",
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"categories.color",
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"categories.topic_count",
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)
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.map
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.with_index do |(id, name, slug, color, topic_count), index|
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{
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id: id,
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name: name,
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slug: slug,
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color: color,
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topicCount: topic_count,
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score: candidates[index].last,
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}
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end
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.map do |c|
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c[:score] = 1 / (c[:score] + 1) # inverse of the distance
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c
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end
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.group_by { |c| c[:name] }
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.map { |name, scores| scores.first.merge(score: scores.sum { |s| s[:score] }) }
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.sort_by { |c| -c[:score] }
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.take(5)
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end
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def tags
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return [] if @text.blank?
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return [] unless SiteSetting.ai_embeddings_enabled
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candidates = nearest_neighbors(limit: 100)
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candidate_ids = candidates.map(&:first)
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count_column = Tag.topic_count_column(@user.guardian) # Determine the count column
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::Topic
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.joins(:topic_tags, :tags)
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.where(id: candidate_ids)
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.where("tags.id IN (?)", DiscourseTagging.visible_tags(@user.guardian).pluck(:id))
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.group("topics.id, tags.id, tags.name") # Group by topics.id and tags.id
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.order("array_position(ARRAY#{candidate_ids}, topics.id)")
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.pluck(
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"tags.id",
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"tags.name",
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"tags.#{count_column}",
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"MIN(array_position(ARRAY#{candidate_ids}, topics.id))", # Get minimum index for ordering
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)
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.uniq # Ensure unique tags per topic
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.map
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.with_index do |(id, name, count, index), idx|
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{
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id: id,
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name: name,
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count: count,
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score: 1 / (candidates[idx].last + 1), # Inverse of the distance for score
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}
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end
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.group_by { |tag| tag[:name] }
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.map do |name, tags|
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tags.first.merge(score: tags.sum { |t| t[:score] })
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end # Aggregate scores per tag
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.sort_by { |tag| -tag[:score] }
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.take(5)
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end
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private
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def nearest_neighbors(limit: 100)
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strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
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vector_rep =
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DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
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raw_vector = vector_rep.vector_from(@text)
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vector_rep.asymmetric_topics_similarity_search(
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raw_vector,
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limit: limit,
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offset: 0,
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return_distance: true,
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)
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end
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end
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end
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end
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