73 lines
2.2 KiB
Ruby
73 lines
2.2 KiB
Ruby
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# frozen_string_literal: true
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module DiscourseAi
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module Embeddings
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class SemanticSuggested
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def self.build_suggested_topics(topic, pm_params, topic_query)
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return unless SiteSetting.ai_embeddings_semantic_suggested_topics_anons_enabled
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return if topic_query.user
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return if topic.private_message?
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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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suggested = search_suggestions(topic)
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# Happens when the topic doesn't have any embeddings
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if suggested.empty? || !suggested.include?(topic.id)
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return { result: [], params: {} }
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end
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suggested
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end
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rescue StandardError => e
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Rails.logger.error("SemanticSuggested: #{e}")
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end
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# array_position forces the order of the topics to be preserved
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candidates =
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::Topic.where(id: candidate_ids).order("array_position(ARRAY#{candidate_ids}, id)")
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{ result: candidates, params: {} }
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end
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def self.search_suggestions(topic)
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model_name = SiteSetting.ai_embeddings_semantic_suggested_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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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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end
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end
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end
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end
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