31 lines
1.1 KiB
Ruby
31 lines
1.1 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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strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
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vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
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vector_rep.consider_indexing(memory: args[:work_mem] || "100MB")
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end
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