discourse-ai/lib/tasks/modules/embeddings/database.rake

55 lines
1.9 KiB
Ruby

# frozen_string_literal: true
desc "Backfill embeddings for all topics and posts"
task "ai:embeddings:backfill", %i[model concurrency] => [:environment] do |_, args|
public_categories = Category.where(read_restricted: false).pluck(:id)
strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
if args[:model].present?
vector_rep =
DiscourseAi::Embeddings::VectorRepresentations::Base.find_representation(args[:model]).new(
strategy,
)
else
vector_rep =
DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
end
table_name = vector_rep.topic_table_name
topics =
Topic
.joins("LEFT JOIN #{table_name} ON #{table_name}.topic_id = topics.id")
.where("#{table_name}.topic_id IS NULL")
.where("category_id IN (?)", public_categories)
.where(deleted_at: nil)
.order("topics.id DESC")
Parallel.each(topics.all, in_processes: args[:concurrency].to_i, progress: "Topics") do |t|
ActiveRecord::Base.connection_pool.with_connection do
vector_rep.generate_representation_from(t)
end
end
table_name = vector_rep.post_table_name
posts =
Post
.joins("LEFT JOIN #{table_name} ON #{table_name}.post_id = posts.id")
.where("#{table_name}.post_id IS NULL")
.where(deleted_at: nil)
.order("posts.id DESC")
Parallel.each(posts.all, in_processes: args[:concurrency].to_i, progress: "Posts") do |t|
ActiveRecord::Base.connection_pool.with_connection do
vector_rep.generate_representation_from(t)
end
end
end
desc "Creates indexes for embeddings"
task "ai:embeddings:index", [:work_mem] => [:environment] do |_, args|
strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
vector_rep.consider_indexing(memory: args[:work_mem] || "100MB")
end