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2c0f535bab
* 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>
73 lines
2.2 KiB
Ruby
73 lines
2.2 KiB
Ruby
# frozen_string_literal: true
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module DiscourseAi
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module Embeddings
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class SemanticRelated
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MissingEmbeddingError = Class.new(StandardError)
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def self.clear_cache_for(topic)
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Discourse.cache.delete("semantic-suggested-topic-#{topic.id}")
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Discourse.redis.del("build-semantic-suggested-topic-#{topic.id}")
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end
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def related_topic_ids_for(topic)
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return [] if SiteSetting.ai_embeddings_semantic_related_topics < 1
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strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
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vector_rep =
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DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
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cache_for = results_ttl(topic)
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asd =
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Discourse
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.cache
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.fetch(semantic_suggested_key(topic.id), expires_in: cache_for) do
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vector_rep
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.symmetric_topics_similarity_search(topic)
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.tap do |candidate_ids|
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# Happens when the topic doesn't have any embeddings
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# I'd rather not use Exceptions to control the flow, so this should be refactored soon
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if candidate_ids.empty? || !candidate_ids.include?(topic.id)
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raise MissingEmbeddingError, "No embeddings found for topic #{topic.id}"
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end
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end
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end
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rescue MissingEmbeddingError
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# avoid a flood of jobs when visiting topic
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if Discourse.redis.set(
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build_semantic_suggested_key(topic.id),
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"queued",
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ex: 15.minutes.to_i,
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nx: true,
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)
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Jobs.enqueue(:generate_embeddings, topic_id: topic.id)
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end
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[]
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end
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def results_ttl(topic)
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case topic.created_at
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when 6.hour.ago..Time.now
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15.minutes
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when 3.day.ago..6.hour.ago
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1.hour
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when 15.days.ago..3.day.ago
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12.hours
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else
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1.week
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end
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end
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private
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def semantic_suggested_key(topic_id)
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"semantic-suggested-topic-#{topic_id}"
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end
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def build_semantic_suggested_key(topic_id)
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"build-semantic-suggested-topic-#{topic_id}"
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end
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end
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end
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end
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