133 lines
3.8 KiB
Ruby
133 lines
3.8 KiB
Ruby
# frozen_string_literal: true
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module Jobs
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class EmbeddingsBackfill < ::Jobs::Scheduled
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every 5.minutes
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sidekiq_options queue: "low"
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cluster_concurrency 1
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def execute(args)
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return unless SiteSetting.ai_embeddings_enabled
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limit = SiteSetting.ai_embeddings_backfill_batch_size
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if limit > 50_000
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limit = 50_000
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Rails.logger.warn(
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"Limiting backfill batch size to 50,000 to avoid OOM errors, reduce ai_embeddings_backfill_batch_size to avoid this warning",
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)
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end
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rebaked = 0
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strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
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vector_rep =
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DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
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table_name = vector_rep.topic_table_name
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topics =
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Topic
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.joins("LEFT JOIN #{table_name} ON #{table_name}.topic_id = topics.id")
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.where(archetype: Archetype.default)
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.where(deleted_at: nil)
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.order("topics.bumped_at DESC")
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.limit(limit - rebaked)
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rebaked += populate_topic_embeddings(vector_rep, topics)
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vector_rep.consider_indexing
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return if rebaked >= limit
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# Then, we'll try to backfill embeddings for topics that have outdated
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# embeddings, be it model or strategy version
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relation = topics.where(<<~SQL)
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#{table_name}.model_version < #{vector_rep.version}
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OR
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#{table_name}.strategy_version < #{strategy.version}
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SQL
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rebaked += populate_topic_embeddings(vector_rep, relation)
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return if rebaked >= limit
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# Finally, we'll try to backfill embeddings for topics that have outdated
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# embeddings due to edits or new replies. Here we only do 10% of the limit
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relation =
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topics.where("#{table_name}.updated_at < ?", 7.days.ago).limit((limit - rebaked) / 10)
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populate_topic_embeddings(vector_rep, relation)
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return if rebaked >= limit
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return unless SiteSetting.ai_embeddings_per_post_enabled
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# Now for posts
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table_name = vector_rep.post_table_name
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posts =
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Post
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.joins("LEFT JOIN #{table_name} ON #{table_name}.post_id = posts.id")
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.where(deleted_at: nil)
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.limit(limit - rebaked)
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# First, we'll try to backfill embeddings for posts that have none
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posts
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.where("#{table_name}.post_id IS NULL")
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.find_each do |t|
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vector_rep.generate_representation_from(t)
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rebaked += 1
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end
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vector_rep.consider_indexing
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return if rebaked >= limit
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# Then, we'll try to backfill embeddings for posts that have outdated
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# embeddings, be it model or strategy version
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posts
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.where(<<~SQL)
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#{table_name}.model_version < #{vector_rep.version}
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OR
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#{table_name}.strategy_version < #{strategy.version}
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SQL
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.find_each do |t|
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vector_rep.generate_representation_from(t)
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rebaked += 1
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end
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return if rebaked >= limit
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# Finally, we'll try to backfill embeddings for posts that have outdated
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# embeddings due to edits. Here we only do 10% of the limit
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posts
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.where("#{table_name}.updated_at < ?", 7.days.ago)
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.order("random()")
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.limit((limit - rebaked) / 10)
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.pluck(:id)
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.each do |id|
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vector_rep.generate_representation_from(Post.find_by(id: id))
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rebaked += 1
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end
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rebaked
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end
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private
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def populate_topic_embeddings(vector_rep, topics)
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done = 0
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ids = topics.where("#{vector_rep.topic_table_name}.topic_id IS NULL").pluck("topics.id")
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ids.each do |id|
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topic = Topic.find_by(id: id)
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if topic
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vector_rep.generate_representation_from(topic)
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done += 1
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end
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end
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done
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end
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end
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end
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