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* FEATURE: HyDE-powered semantic search. It relies on the new outlet added on discourse/discourse#23390 to display semantic search results in an unobtrusive way. We'll use a HyDE-backed approach for semantic search, which consists on generating an hypothetical document from a given keywords, which gets transformed into a vector and used in a asymmetric similarity topic search. This PR also reorganizes the internals to have less moving parts, maintaining one hierarchy of DAOish classes for vector-related operations like transformations and querying. Completions and vectors created by HyDE will remain cached on Redis for now, but we could later use Postgres instead. * Missing translation and rate limiting --------- Co-authored-by: Roman Rizzi <rizziromanalejandro@gmail.com>
29 lines
883 B
Ruby
29 lines
883 B
Ruby
# frozen_string_literal: true
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class DiscourseAi::Embeddings::SemanticTopicQuery < TopicQuery
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def list_semantic_related_topics(topic)
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query_opts = {
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skip_ordering: true,
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per_page: SiteSetting.ai_embeddings_semantic_related_topics,
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unordered: true,
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}
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if !SiteSetting.ai_embeddings_semantic_related_include_closed_topics
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query_opts[:status] = "open"
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end
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list =
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create_list(:semantic_related, query_opts) do |topics|
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candidate_ids = DiscourseAi::Embeddings::SemanticRelated.new.related_topic_ids_for(topic)
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list =
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topics
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.where.not(id: topic.id)
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.where(id: candidate_ids)
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.order("array_position(ARRAY#{candidate_ids}, topics.id)") # array_position forces the order of the topics to be preserved
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list = remove_muted(list, @user, query_opts)
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end
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end
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end
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