# frozen_string_literal: true module DiscourseAi module Embeddings class SemanticRelated MissingEmbeddingError = Class.new(StandardError) def self.clear_cache_for(topic) Discourse.cache.delete("semantic-suggested-topic-#{topic.id}") Discourse.redis.del("build-semantic-suggested-topic-#{topic.id}") end def related_topic_ids_for(topic) return [] if SiteSetting.ai_embeddings_semantic_related_topics < 1 strategy = DiscourseAi::Embeddings::Strategies::Truncation.new vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy) cache_for = results_ttl(topic) asd = Discourse .cache .fetch(semantic_suggested_key(topic.id), expires_in: cache_for) do vector_rep .symmetric_topics_similarity_search(topic) .tap do |candidate_ids| # Happens when the topic doesn't have any embeddings # I'd rather not use Exceptions to control the flow, so this should be refactored soon if candidate_ids.empty? || !candidate_ids.include?(topic.id) raise MissingEmbeddingError, "No embeddings found for topic #{topic.id}" end end end rescue MissingEmbeddingError # avoid a flood of jobs when visiting topic if Discourse.redis.set( build_semantic_suggested_key(topic.id), "queued", ex: 15.minutes.to_i, nx: true, ) Jobs.enqueue(:generate_embeddings, topic_id: topic.id) end [] end def results_ttl(topic) case topic.created_at when 6.hour.ago..Time.now 15.minutes when 3.day.ago..6.hour.ago 1.hour when 15.days.ago..3.day.ago 12.hours else 1.week end end private def semantic_suggested_key(topic_id) "semantic-suggested-topic-#{topic_id}" end def build_semantic_suggested_key(topic_id) "build-semantic-suggested-topic-#{topic_id}" end end end end