From ee734a340a6f468de65465c1fddede0f9801214b Mon Sep 17 00:00:00 2001
From: Rafael dos Santos Silva <xfalcox@gmail.com>
Date: Tue, 5 Sep 2023 14:15:01 -0300
Subject: [PATCH] FIX: Tag/category suggestion broke in 2c0f535 (#198)

---
 lib/modules/ai_helper/semantic_categorizer.rb | 32 +++++++++++--------
 1 file changed, 19 insertions(+), 13 deletions(-)

diff --git a/lib/modules/ai_helper/semantic_categorizer.rb b/lib/modules/ai_helper/semantic_categorizer.rb
index c3918c5d..6a19075b 100644
--- a/lib/modules/ai_helper/semantic_categorizer.rb
+++ b/lib/modules/ai_helper/semantic_categorizer.rb
@@ -11,12 +11,7 @@ module DiscourseAi
         return [] if @text.blank?
         return [] unless SiteSetting.ai_embeddings_enabled
 
-        strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
-        vector_rep =
-          DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
-
-        candidates =
-          vector_rep.asymmetric_semantic_search(@text, limit: 100, offset: 0, return_distance: true)
+        candidates = nearest_neighbors(limit: 100)
         candidate_ids = candidates.map(&:first)
 
         ::Topic
@@ -41,13 +36,7 @@ module DiscourseAi
         return [] if @text.blank?
         return [] unless SiteSetting.ai_embeddings_enabled
 
-        candidates =
-          ::DiscourseAi::Embeddings::SemanticSearch.new(nil).asymmetric_semantic_search(
-            @text,
-            100,
-            0,
-            return_distance: true,
-          )
+        candidates = nearest_neighbors(limit: 100)
         candidate_ids = candidates.map(&:first)
 
         ::Topic
@@ -70,6 +59,23 @@ module DiscourseAi
           .sort_by { |c| -c[:score] }
           .take(5)
       end
+
+      private
+
+      def nearest_neighbors(limit: 100)
+        strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
+        vector_rep =
+          DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
+
+        raw_vector = vector_rep.vector_from(@text)
+
+        vector_rep.asymmetric_topics_similarity_search(
+          raw_vector,
+          limit: limit,
+          offset: 0,
+          return_distance: true,
+        )
+      end
     end
   end
 end