discourse-ai/lib/modules/embeddings/semantic_search.rb

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# frozen_string_literal: true
module DiscourseAi
module Embeddings
class SemanticSearch
def self.clear_cache_for(query)
digest = OpenSSL::Digest::SHA1.hexdigest(query)
Discourse.cache.delete("hyde-doc-#{digest}")
Discourse.cache.delete("hyde-doc-embedding-#{digest}")
end
def initialize(guardian)
@guardian = guardian
end
def cached_query?(query)
digest = OpenSSL::Digest::SHA1.hexdigest(query)
Discourse.cache.read("hyde-doc-embedding-#{digest}").present?
end
def search_for_topics(query, page = 1)
max_results_per_page = 50
limit = [Search.per_filter, max_results_per_page].min + 1
offset = (page - 1) * limit
strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
vector_rep =
DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
digest = OpenSSL::Digest::SHA1.hexdigest(query)
hypothetical_post =
Discourse
.cache
.fetch("hyde-doc-#{digest}", expires_in: 1.week) do
hyde_generator = DiscourseAi::Embeddings::HydeGenerators::Base.current_hyde_model.new
hyde_generator.hypothetical_post_from(query)
end
hypothetical_post_embedding =
Discourse
.cache
.fetch("hyde-doc-embedding-#{digest}", expires_in: 1.week) do
vector_rep.vector_from(hypothetical_post)
end
candidate_topic_ids =
vector_rep.asymmetric_topics_similarity_search(
hypothetical_post_embedding,
limit: limit,
offset: offset,
)
::Post
.where(post_type: ::Topic.visible_post_types(guardian.user))
.public_posts
.where("topics.visible")
.where(topic_id: candidate_topic_ids, post_number: 1)
.order("array_position(ARRAY#{candidate_topic_ids}, topic_id)")
end
private
attr_reader :guardian
end
end
end