FEATURE: exclude muted categories from category suggester (#979)

The logic here is that users do not particularly care about
topics in the category so we can exclude them from tag
and category suggestions

Author: SamSaffron

lib/ai_helper/semantic_categorizer.rb

@@ -12,6 +12,8 @@ def categories
         return [] unless SiteSetting.ai_embeddings_enabled
 
         candidates = nearest_neighbors(limit: 100)
+        return [] if candidates.empty?
+
         candidate_ids = candidates.map(&:first)
 
         ::Topic
@@ -52,6 +54,8 @@ def tags
         return [] unless SiteSetting.ai_embeddings_enabled
 
         candidates = nearest_neighbors(limit: 100)
+        return [] if candidates.empty?
+
         candidate_ids = candidates.map(&:first)
 
         count_column = Tag.topic_count_column(@user.guardian) # Determine the count column
@@ -94,11 +98,21 @@ def nearest_neighbors(limit: 100)
 
         raw_vector = vector_rep.vector_from(@text)
 
+        muted_category_ids = nil
+        if @user.present?
+          muted_category_ids =
+            CategoryUser.where(
+              user: @user,
+              notification_level: CategoryUser.notification_levels[:muted],
+            ).pluck(:category_id)
+        end
+
         vector_rep.asymmetric_topics_similarity_search(
           raw_vector,
           limit: limit,
           offset: 0,
           return_distance: true,
+          exclude_category_ids: muted_category_ids,
         )
       end
     end

lib/embeddings/vector_representations/base.rb

@@ -151,16 +151,22 @@ def post_id_from_representation(raw_vector)
           SQL
         end
 
-        def asymmetric_topics_similarity_search(raw_vector, limit:, offset:, return_distance: false)
-          results = DB.query(<<~SQL, query_embedding: raw_vector, limit: limit, offset: offset)
+        def asymmetric_topics_similarity_search(
+          raw_vector,
+          limit:,
+          offset:,
+          return_distance: false,
+          exclude_category_ids: nil
+        )
+          builder = DB.build(<<~SQL)
             WITH candidates AS (
               SELECT
                 topic_id,
                 embeddings::halfvec(#{dimensions}) AS embeddings
               FROM
                 #{topic_table_name}
-              WHERE
-                model_id = #{id} AND strategy_id = #{@strategy.id}
+              /*join*/
+              /*where*/
               ORDER BY
                 binary_quantize(embeddings)::bit(#{dimensions}) <~> binary_quantize('[:query_embedding]'::halfvec(#{dimensions}))
               LIMIT :limit * 2
@@ -176,6 +182,22 @@ def asymmetric_topics_similarity_search(raw_vector, limit:, offset:, return_dist
             OFFSET :offset
           SQL
 
+          builder.where(
+            "model_id = :model_id AND strategy_id = :strategy_id",
+            model_id: id,
+            strategy_id: @strategy.id,
+          )
+
+          if exclude_category_ids.present?
+            builder.join("topics t on t.id = topic_id")
+            builder.where(<<~SQL, exclude_category_ids: exclude_category_ids.map(&:to_i))
+                t.category_id NOT IN (:exclude_category_ids) AND
+                t.category_id NOT IN (SELECT categories.id FROM categories WHERE categories.parent_category_id IN (:exclude_category_ids))
+            SQL
+          end
+
+          results = builder.query(query_embedding: raw_vector, limit: limit, offset: offset)
+
           if return_distance
             results.map { |r| [r.topic_id, r.distance] }
           else

spec/lib/modules/ai_helper/semantic_categorizer_spec.rb

@@ -0,0 +1,43 @@
+# frozen_string_literal: true
+
+RSpec.describe DiscourseAi::AiHelper::SemanticCategorizer do
+  fab!(:user)
+  fab!(:muted_category) { Fabricate(:category) }
+  fab!(:category_mute) do
+    CategoryUser.create!(
+      user: user,
+      category: muted_category,
+      notification_level: CategoryUser.notification_levels[:muted],
+    )
+  end
+  fab!(:muted_topic) { Fabricate(:topic, category: muted_category) }
+  fab!(:category)
+  fab!(:topic) { Fabricate(:topic, category: category) }
+
+  let(:truncation) { DiscourseAi::Embeddings::Strategies::Truncation.new }
+  let(:vector_rep) do
+    DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(truncation)
+  end
+  let(:categorizer) { DiscourseAi::AiHelper::SemanticCategorizer.new({ text: "hello" }, user) }
+  let(:expected_embedding) { [0.0038493] * vector_rep.dimensions }
+
+  before do
+    SiteSetting.ai_embeddings_enabled = true
+    SiteSetting.ai_embeddings_discourse_service_api_endpoint = "http://test.com"
+    SiteSetting.ai_embeddings_model = "bge-large-en"
+
+    WebMock.stub_request(
+      :post,
+      "#{SiteSetting.ai_embeddings_discourse_service_api_endpoint}/api/v1/classify",
+    ).to_return(status: 200, body: JSON.dump(expected_embedding))
+
+    vector_rep.generate_representation_from(topic)
+    vector_rep.generate_representation_from(muted_topic)
+  end
+
+  it "respects user muted categories when making suggestions" do
+    category_ids = categorizer.categories.map { |c| c[:id] }
+    expect(category_ids).not_to include(muted_category.id)
+    expect(category_ids).to include(category.id)
+  end
+end

spec/lib/modules/embeddings/vector_representations/vector_rep_shared_examples.rb

@@ -104,5 +104,38 @@
         vector_rep.asymmetric_topics_similarity_search(similar_vector, limit: 1, offset: 0),
       ).to contain_exactly(topic.id)
     end
+
+    it "can exclude categories" do
+      similar_vector = [0.0038494] * vector_rep.dimensions
+      text =
+        truncation.prepare_text_from(
+          topic,
+          vector_rep.tokenizer,
+          vector_rep.max_sequence_length - 2,
+        )
+      stub_vector_mapping(text, expected_embedding_1)
+      vector_rep.generate_representation_from(topic)
+
+      expect(
+        vector_rep.asymmetric_topics_similarity_search(
+          similar_vector,
+          limit: 1,
+          offset: 0,
+          exclude_category_ids: [topic.category_id],
+        ),
+      ).to be_empty
+
+      child_category = Fabricate(:category, parent_category_id: topic.category_id)
+      topic.update!(category_id: child_category.id)
+
+      expect(
+        vector_rep.asymmetric_topics_similarity_search(
+          similar_vector,
+          limit: 1,
+          offset: 0,
+          exclude_category_ids: [topic.category_id],
+        ),
+      ).to be_empty
+    end
   end
 end