59 lines
2.1 KiB
Ruby
59 lines
2.1 KiB
Ruby
# frozen_string_literal: true
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desc "Creates tables to store embeddings"
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task "ai:embeddings:create_table" => [:environment] do
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DiscourseAi::Database::Connection.db.exec(<<~SQL)
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CREATE EXTENSION IF NOT EXISTS vector;
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SQL
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DiscourseAi::Embeddings::Model.enabled_models.each do |model|
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DiscourseAi::Database::Connection.db.exec(<<~SQL)
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CREATE TABLE IF NOT EXISTS topic_embeddings_#{model.name.underscore} (
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topic_id bigint PRIMARY KEY,
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embedding vector(#{model.dimensions})
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);
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SQL
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end
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end
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desc "Backfill embeddings for all topics"
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task "ai:embeddings:backfill", [:start_topic] => [:environment] do
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public_categories = Category.where(read_restricted: false).pluck(:id)
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topic_embeddings = DiscourseAi::Embeddings::Topic.new
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Topic
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.where("id >= ?", args[:start_topic] || 0)
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.where("category_id IN (?)", public_categories)
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.where(deleted_at: nil)
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.order(id: :asc)
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.find_each do |t|
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print "."
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topic_embeddings.generate_and_store_embeddings_for(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 double 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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DiscourseAi::Database::Connection.db.exec("SET work_mem TO '#{args[:work_mem] || "1GB"}';")
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DiscourseAi::Embeddings::Model.enabled_models.each do |model|
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DiscourseAi::Database::Connection.db.exec(<<~SQL)
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CREATE INDEX IF NOT EXISTS
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topic_embeddings_#{model.name.underscore}_search
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ON
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topic_embeddings_#{model.name.underscore}
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USING
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ivfflat (embedding #{model.pg_index})
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WITH
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(lists = #{lists});
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SQL
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end
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DiscourseAi::Database::Connection.db.exec("RESET work_mem;")
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DiscourseAi::Database::Connection.db.exec("SET ivfflat.probes = #{probes};")
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end
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