79 lines
2.4 KiB
Ruby
79 lines
2.4 KiB
Ruby
# frozen_string_literal: true
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module DiscourseAi
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module Embeddings
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class SemanticRelated
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def self.candidates_for(topic)
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return ::Topic.none if SiteSetting.ai_embeddings_semantic_related_topics < 1
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cache_for =
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case topic.created_at
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when 6.hour.ago..Time.now
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15.minutes
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when 1.day.ago..6.hour.ago
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1.hour
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else
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1.day
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end
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begin
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candidate_ids =
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Discourse
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.cache
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.fetch("semantic-suggested-topic-#{topic.id}", expires_in: cache_for) do
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search_suggestions(topic)
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end
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rescue StandardError => e
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Rails.logger.error("SemanticRelated: #{e}")
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Jobs.enqueue(:generate_embeddings, topic_id: topic.id)
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return ::Topic.none
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end
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# array_position forces the order of the topics to be preserved
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::Topic
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.visible
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.listable_topics
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.secured
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.where("id <> ?", topic.id)
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.where(id: candidate_ids)
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.order("array_position(ARRAY#{candidate_ids}, id)")
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.limit(SiteSetting.ai_embeddings_semantic_related_topics)
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end
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def self.search_suggestions(topic)
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model_name = SiteSetting.ai_embeddings_semantic_related_model
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model = DiscourseAi::Embeddings::Models.list.find { |m| m.name == model_name }
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function =
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DiscourseAi::Embeddings::Models::SEARCH_FUNCTION_TO_PG_FUNCTION[model.functions.first]
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candidate_ids =
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DiscourseAi::Database::Connection.db.query(<<~SQL, topic_id: topic.id).map(&:topic_id)
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SELECT
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topic_id
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FROM
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topic_embeddings_#{model_name.underscore}
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ORDER BY
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embedding #{function} (
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SELECT
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embedding
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FROM
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topic_embeddings_#{model_name.underscore}
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WHERE
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topic_id = :topic_id
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LIMIT 1
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)
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LIMIT 11
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SQL
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# Happens when the topic doesn't have any embeddings
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# I'd rather not use Exceptions to control the flow, so this should be refactored soon
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if candidate_ids.empty? || !candidate_ids.include?(topic.id)
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raise StandardError, "No embeddings found for topic #{topic.id}"
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end
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candidate_ids
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end
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end
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end
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end
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