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4e05763a99
Depends on discourse/discourse#20915 Hooks to the full-page-search component using an experimental API and performs an assymetric similarity search using our embeddings database.
50 lines
1.4 KiB
Ruby
50 lines
1.4 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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model =
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DiscourseAi::Embeddings::Model.instantiate(
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SiteSetting.ai_embeddings_semantic_related_model,
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)
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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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DiscourseAi::Embeddings::Topic.new.symmetric_semantic_search(model, 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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end
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end
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end
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