discourse-ai/lib/embeddings/semantic_related.rb

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# 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