41 lines
1.6 KiB
Ruby
41 lines
1.6 KiB
Ruby
# frozen_string_literal: true
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desc "Backfill embeddings for all topics"
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task "ai:embeddings:backfill", [:start_topic] => [:environment] do |_, args|
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public_categories = Category.where(read_restricted: false).pluck(:id)
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strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
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vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
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table_name = vector_rep.table_name
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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("#{table_name}.topic_id IS NULL")
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.where("topics.id >= ?", args[:start_topic].to_i || 0)
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.where("category_id IN (?)", public_categories)
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.where(deleted_at: nil)
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.order("topics.id ASC")
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.find_each do |t|
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print "."
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vector_rep.generate_topic_representation_from(t)
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end
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end
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desc "Creates indexes for embeddings"
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task "ai:embeddings:index", [:work_mem] => [:environment] do |_, args|
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# Using extension maintainer's recommendation for ivfflat indexes
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# Results are not as good as without indexes, but it's much faster
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# Disk usage is ~1x the size of the table, so this doubles table total size
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count = Topic.count
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lists = count < 1_000_000 ? count / 1000 : Math.sqrt(count).to_i
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probes = count < 1_000_000 ? lists / 10 : Math.sqrt(lists).to_i
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vector_representation_klass = DiscourseAi::Embeddings::Vectors::Base.find_vector_representation
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strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
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DB.exec("SET work_mem TO '#{args[:work_mem] || "1GB"}';")
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vector_representation_klass.new(strategy).create_index(lists, probes)
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DB.exec("RESET work_mem;")
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DB.exec("SET ivfflat.probes = #{probes};")
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end
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