diff --git a/lib/embeddings/vector_representations/base.rb b/lib/embeddings/vector_representations/base.rb
index 831a9870..622bbebb 100644
--- a/lib/embeddings/vector_representations/base.rb
+++ b/lib/embeddings/vector_representations/base.rb
@@ -66,13 +66,16 @@ module DiscourseAi
                 prepared_text = prepare_text(record)
                 next if prepared_text.blank?
 
+                new_digest = OpenSSL::Digest::SHA1.hexdigest(prepared_text)
+                next if find_digest_of(record) == new_digest
+
                 Concurrent::Promises
-                  .fulfilled_future({ target: record, text: prepared_text }, pool)
+                  .fulfilled_future(
+                    { target: record, text: prepared_text, digest: new_digest },
+                    pool,
+                  )
                   .then_on(pool) do |w_prepared_text|
-                    w_prepared_text.merge(
-                      embedding: embedding_gen.perform!(w_prepared_text[:text]),
-                      digest: OpenSSL::Digest::SHA1.hexdigest(w_prepared_text[:text]),
-                    )
+                    w_prepared_text.merge(embedding: embedding_gen.perform!(w_prepared_text[:text]))
                   end
               end
               .compact
@@ -90,31 +93,8 @@ module DiscourseAi
           text = prepare_text(target)
           return if text.blank?
 
-          target_column =
-            case target
-            when Topic
-              "topic_id"
-            when Post
-              "post_id"
-            when RagDocumentFragment
-              "rag_document_fragment_id"
-            else
-              raise ArgumentError, "Invalid target type"
-            end
-
           new_digest = OpenSSL::Digest::SHA1.hexdigest(text)
-          current_digest = DB.query_single(<<~SQL, target_id: target.id).first
-            SELECT
-              digest
-            FROM
-              #{table_name(target)}
-            WHERE
-              model_id = #{id} AND
-              strategy_id = #{@strategy.id} AND
-              #{target_column} = :target_id
-            LIMIT 1
-          SQL
-          return if current_digest == new_digest
+          return if find_digest_of(target) == new_digest
 
           vector = vector_from(text)
 
@@ -412,6 +392,32 @@ module DiscourseAi
 
         protected
 
+        def find_digest_of(target)
+          target_column =
+            case target
+            when Topic
+              "topic_id"
+            when Post
+              "post_id"
+            when RagDocumentFragment
+              "rag_document_fragment_id"
+            else
+              raise ArgumentError, "Invalid target type"
+            end
+
+          DB.query_single(<<~SQL, target_id: target.id).first
+            SELECT
+              digest
+            FROM
+              #{table_name(target)}
+            WHERE
+              model_id = #{id} AND
+              strategy_id = #{@strategy.id} AND
+              #{target_column} = :target_id
+            LIMIT 1
+          SQL
+        end
+
         def save_to_db(target, vector, digest)
           if target.is_a?(Topic)
             DB.exec(
diff --git a/spec/lib/modules/embeddings/vector_representations/vector_rep_shared_examples.rb b/spec/lib/modules/embeddings/vector_representations/vector_rep_shared_examples.rb
index a9134403..714a9189 100644
--- a/spec/lib/modules/embeddings/vector_representations/vector_rep_shared_examples.rb
+++ b/spec/lib/modules/embeddings/vector_representations/vector_rep_shared_examples.rb
@@ -83,6 +83,32 @@ RSpec.shared_examples "generates and store embedding using with vector represent
     it "does nothing if passed record has no content" do
       expect { vector_rep.gen_bulk_reprensentations([Topic.new]) }.not_to raise_error
     end
+
+    it "doesn't ask for a new embedding if digest is the same" do
+      text =
+        truncation.prepare_text_from(
+          topic,
+          vector_rep.tokenizer,
+          vector_rep.max_sequence_length - 2,
+        )
+      stub_vector_mapping(text, expected_embedding_1)
+
+      original_vector_gen = Time.zone.parse("2021-06-04 10:00")
+
+      freeze_time(original_vector_gen) do
+        vector_rep.gen_bulk_reprensentations(Topic.where(id: [topic.id]))
+      end
+      # check vector exists
+      expect(vector_rep.topic_id_from_representation(expected_embedding_1)).to eq(topic.id)
+
+      vector_rep.gen_bulk_reprensentations(Topic.where(id: [topic.id]))
+      last_update =
+        DB.query_single(
+          "SELECT updated_at FROM #{vector_rep.topic_table_name} WHERE topic_id = #{topic.id} LIMIT 1",
+        ).first
+
+      expect(last_update).to eq(original_vector_gen)
+    end
   end
 
   describe "#asymmetric_topics_similarity_search" do