73 lines
2.1 KiB
Ruby
73 lines
2.1 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.semantic_suggested_key(topic_id)
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"semantic-suggested-topic-#{topic_id}"
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end
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def self.build_semantic_suggested_key(topic_id)
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"build-semantic-suggested-topic-#{topic_id}"
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end
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def self.clear_cache_for(topic)
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Discourse.cache.delete(semantic_suggested_key(topic.id))
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Discourse.redis.del(build_semantic_suggested_key(topic.id))
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end
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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_key(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 MissingEmbeddingError
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# avoid a flood of jobs when visiting topic
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if Discourse.redis.set(
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build_semantic_suggested_key(topic.id),
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"queued",
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ex: 15.minutes.to_i,
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nx: true,
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)
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Jobs.enqueue(:generate_embeddings, topic_id: topic.id)
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end
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return ::Topic.none
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end
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topic_list = ::Topic.visible.listable_topics.secured
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unless SiteSetting.ai_embeddings_semantic_related_include_closed_topics
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topic_list = topic_list.where(closed: false)
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end
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topic_list
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.where("id <> ?", topic.id)
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.where(id: candidate_ids)
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# array_position forces the order of the topics to be preserved
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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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