REFACTOR: Separation of concerns for embedding generation.

In a previous refactor, we moved the responsibility of querying and storing embeddings into the `Schema` class. Now, it's time for embedding generation.

The motivation behind these changes is to isolate vector characteristics in simple objects to later replace them with a DB-backed version, similar to what we did with LLM configs.

app/jobs/regular/generate_embeddings.rb

@@ -16,9 +16,7 @@ def execute(args)
       return if topic.private_message? && !SiteSetting.ai_embeddings_generate_for_pms
       return if post.raw.blank?
 
-      vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation
-
-      vector_rep.generate_representation_from(target)
+      DiscourseAi::Embeddings::Vector.instance.generate_representation_from(target)
     end
   end
 end

app/jobs/regular/generate_rag_embeddings.rb

@@ -8,11 +8,11 @@ class GenerateRagEmbeddings < ::Jobs::Base
     def execute(args)
       return if (fragments = RagDocumentFragment.where(id: args[:fragment_ids].to_a)).empty?
 
-      vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation
+      vector = DiscourseAi::Embeddings::Vector.instance
 
       # generate_representation_from checks compares the digest value to make sure
       # the embedding is only generated once per fragment unless something changes.
-      fragments.map { |fragment| vector_rep.generate_representation_from(fragment) }
+      fragments.map { |fragment| vector.generate_representation_from(fragment) }
 
       last_fragment = fragments.last
       target = last_fragment.target

app/jobs/scheduled/embeddings_backfill.rb

@@ -20,7 +20,8 @@ def execute(args)
 
       rebaked = 0
 
-      vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation
+      vector = DiscourseAi::Embeddings::Vector.instance
+      vector_def = vector.vdef
       table_name = DiscourseAi::Embeddings::Schema::TOPICS_TABLE
 
       topics =
@@ -30,19 +31,19 @@ def execute(args)
           .where(deleted_at: nil)
           .order("topics.bumped_at DESC")
 
-      rebaked += populate_topic_embeddings(vector_rep, topics.limit(limit - rebaked))
+      rebaked += populate_topic_embeddings(vector, topics.limit(limit - rebaked))
 
       return if rebaked >= limit
 
       # Then, we'll try to backfill embeddings for topics that have outdated
       # embeddings, be it model or strategy version
       relation = topics.where(<<~SQL).limit(limit - rebaked)
-          #{table_name}.model_version < #{vector_rep.version}
+          #{table_name}.model_version < #{vector_def.version}
           OR
-          #{table_name}.strategy_version < #{vector_rep.strategy_version}
+          #{table_name}.strategy_version < #{vector_def.strategy_version}
         SQL
 
-      rebaked += populate_topic_embeddings(vector_rep, relation)
+      rebaked += populate_topic_embeddings(vector, relation)
 
       return if rebaked >= limit
 
@@ -54,7 +55,7 @@ def execute(args)
           .where("#{table_name}.updated_at < topics.updated_at")
           .limit((limit - rebaked) / 10)
 
-      populate_topic_embeddings(vector_rep, relation, force: true)
+      populate_topic_embeddings(vector, relation, force: true)
 
       return if rebaked >= limit
 
@@ -76,7 +77,7 @@ def execute(args)
         .limit(limit - rebaked)
         .pluck(:id)
         .each_slice(posts_batch_size) do |batch|
-          vector_rep.gen_bulk_reprensentations(Post.where(id: batch))
+          vector.gen_bulk_reprensentations(Post.where(id: batch))
           rebaked += batch.length
         end
 
@@ -86,14 +87,14 @@ def execute(args)
       # embeddings, be it model or strategy version
       posts
         .where(<<~SQL)
-          #{table_name}.model_version < #{vector_rep.version}
+          #{table_name}.model_version < #{vector_def.version}
           OR
-          #{table_name}.strategy_version < #{strategy.version}
+          #{table_name}.strategy_version < #{vector_def.strategy_version}
         SQL
         .limit(limit - rebaked)
         .pluck(:id)
         .each_slice(posts_batch_size) do |batch|
-          vector_rep.gen_bulk_reprensentations(Post.where(id: batch))
+          vector.gen_bulk_reprensentations(Post.where(id: batch))
           rebaked += batch.length
         end
 
@@ -107,7 +108,7 @@ def execute(args)
         .limit((limit - rebaked) / 10)
         .pluck(:id)
         .each_slice(posts_batch_size) do |batch|
-          vector_rep.gen_bulk_reprensentations(Post.where(id: batch))
+          vector.gen_bulk_reprensentations(Post.where(id: batch))
           rebaked += batch.length
         end
 
@@ -116,7 +117,7 @@ def execute(args)
 
     private
 
-    def populate_topic_embeddings(vector_rep, topics, force: false)
+    def populate_topic_embeddings(vector, topics, force: false)
       done = 0
 
       topics =
@@ -126,7 +127,7 @@ def populate_topic_embeddings(vector_rep, topics, force: false)
       batch_size = 1000
 
       ids.each_slice(batch_size) do |batch|
-        vector_rep.gen_bulk_reprensentations(Topic.where(id: batch).order("topics.bumped_at DESC"))
+        vector.gen_bulk_reprensentations(Topic.where(id: batch).order("topics.bumped_at DESC"))
         done += batch.length
       end
 

lib/ai_bot/personas/persona.rb

@@ -314,10 +314,10 @@ def rag_fragments_prompt(conversation_context, llm:, user:)
 
           return nil if !consolidated_question
 
-          vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation
+          vector = DiscourseAi::Embeddings::Vector.instance
           reranker = DiscourseAi::Inference::HuggingFaceTextEmbeddings
 
-          interactions_vector = vector_rep.vector_from(consolidated_question)
+          interactions_vector = vector.vector_from(consolidated_question)
 
           rag_conversation_chunks = self.class.rag_conversation_chunks
           search_limit =
@@ -327,7 +327,7 @@ def rag_fragments_prompt(conversation_context, llm:, user:)
               rag_conversation_chunks
             end
 
-          schema = DiscourseAi::Embeddings::Schema.for(RagDocumentFragment, vector: vector_rep)
+          schema = DiscourseAi::Embeddings::Schema.for(RagDocumentFragment, vector_def: vector.vdef)
 
           candidate_fragment_ids =
             schema

lib/ai_bot/tool_runner.rb

@@ -141,11 +141,10 @@ def rag_search(query, filenames: nil, limit: 10)
 
         return [] if upload_refs.empty?
 
-        vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation
-        query_vector = vector_rep.vector_from(query)
+        query_vector = DiscourseAi::Embeddings::Vector.instance.vector_from(query)
         fragment_ids =
           DiscourseAi::Embeddings::Schema
-            .for(RagDocumentFragment, vector: vector_rep)
+            .for(RagDocumentFragment)
             .asymmetric_similarity_search(query_vector, limit: limit, offset: 0) do |builder|
               builder.join(<<~SQL, target_id: tool.id, target_type: "AiTool")
                 rag_document_fragments ON

lib/ai_helper/semantic_categorizer.rb

@@ -92,10 +92,10 @@ def tags
       private
 
       def nearest_neighbors(limit: 100)
-        vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation
-        schema = DiscourseAi::Embeddings::Schema.for(Topic, vector: vector_rep)
+        vector = DiscourseAi::Embeddings::Vector.instance
+        schema = DiscourseAi::Embeddings::Schema.for(Topic, vector_def: vector.vdef)
 
-        raw_vector = vector_rep.vector_from(@text)
+        raw_vector = vector.vector_from(@text)
 
         muted_category_ids = nil
         if @user.present?

lib/embeddings/schema.rb

@@ -14,30 +14,31 @@ class Schema
 
       def self.for(
         target_klass,
-        vector: DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation
+        vector_def: DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation
       )
         case target_klass&.name
         when "Topic"
-          new(TOPICS_TABLE, "topic_id", vector)
+          new(TOPICS_TABLE, "topic_id", vector_def)
         when "Post"
-          new(POSTS_TABLE, "post_id", vector)
+          new(POSTS_TABLE, "post_id", vector_def)
         when "RagDocumentFragment"
-          new(RAG_DOCS_TABLE, "rag_document_fragment_id", vector)
+          new(RAG_DOCS_TABLE, "rag_document_fragment_id", vector_def)
         else
           raise ArgumentError, "Invalid target type for embeddings"
         end
       end
 
-      def initialize(table, target_column, vector)
+      def initialize(table, target_column, vector_def)
         @table = table
         @target_column = target_column
-        @vector = vector
+        @vector_def = vector_def
       end
 
-      attr_reader :table, :target_column, :vector
+      attr_reader :table, :target_column, :vector_def
 
       def find_by_embedding(embedding)
-        DB.query(<<~SQL, query_embedding: embedding, vid: vector.id, vsid: vector.strategy_id).first
+        DB.query(
+          <<~SQL,
           SELECT *
           FROM #{table}
           WHERE
@@ -46,10 +47,15 @@ def find_by_embedding(embedding)
             embeddings::halfvec(#{dimensions}) #{pg_function} '[:query_embedding]'::halfvec(#{dimensions})
           LIMIT 1
         SQL
+          query_embedding: embedding,
+          vid: vector_def.id,
+          vsid: vector_def.strategy_id,
+        ).first
       end
 
       def find_by_target(target)
-        DB.query(<<~SQL, target_id: target.id, vid: vector.id, vsid: vector.strategy_id).first
+        DB.query(
+          <<~SQL,
           SELECT *
           FROM #{table}
           WHERE
@@ -58,6 +64,10 @@ def find_by_target(target)
             #{target_column} = :target_id
           LIMIT 1
         SQL
+          target_id: target.id,
+          vid: vector_def.id,
+          vsid: vector_def.strategy_id,
+        ).first
       end
 
       def asymmetric_similarity_search(embedding, limit:, offset:)
@@ -87,8 +97,8 @@ def asymmetric_similarity_search(embedding, limit:, offset:)
 
         builder.where(
           "model_id = :model_id AND strategy_id = :strategy_id",
-          model_id: vector.id,
-          strategy_id: vector.strategy_id,
+          model_id: vector_def.id,
+          strategy_id: vector_def.strategy_id,
         )
 
         yield(builder) if block_given?
@@ -156,7 +166,7 @@ def symmetric_similarity_search(record)
 
         yield(builder) if block_given?
 
-        builder.query(vid: vector.id, vsid: vector.strategy_id, target_id: record.id)
+        builder.query(vid: vector_def.id, vsid: vector_def.strategy_id, target_id: record.id)
       rescue PG::Error => e
         Rails.logger.error("Error #{e} querying embeddings for model #{name}")
         raise MissingEmbeddingError
@@ -176,10 +186,10 @@ def store(record, embedding, digest)
             updated_at = :now
           SQL
           target_id: record.id,
-          model_id: vector.id,
-          model_version: vector.version,
-          strategy_id: vector.strategy_id,
-          strategy_version: vector.strategy_version,
+          model_id: vector_def.id,
+          model_version: vector_def.version,
+          strategy_id: vector_def.strategy_id,
+          strategy_version: vector_def.strategy_version,
           digest: digest,
           embeddings: embedding,
           now: Time.zone.now,
@@ -188,7 +198,7 @@ def store(record, embedding, digest)
 
       private
 
-      delegate :dimensions, :pg_function, to: :vector
+      delegate :dimensions, :pg_function, to: :vector_def
     end
   end
 end

lib/embeddings/semantic_related.rb

@@ -13,14 +13,13 @@ def self.clear_cache_for(topic)
       def related_topic_ids_for(topic)
         return [] if SiteSetting.ai_embeddings_semantic_related_topics < 1
 
-        vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation
         cache_for = results_ttl(topic)
 
         Discourse
           .cache
           .fetch(semantic_suggested_key(topic.id), expires_in: cache_for) do
             DiscourseAi::Embeddings::Schema
-              .for(Topic, vector: vector_rep)
+              .for(Topic)
               .symmetric_similarity_search(topic)
               .map(&:topic_id)
               .tap do |candidate_ids|

lib/embeddings/semantic_search.rb

@@ -30,8 +30,8 @@ def cached_query?(query)
         Discourse.cache.read(embedding_key).present?
       end
 
-      def vector_rep
-        @vector_rep ||= DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation
+      def vector
+        @vector ||= DiscourseAi::Embeddings::Vector.instance
       end
 
       def hyde_embedding(search_term)
@@ -52,16 +52,14 @@ def hyde_embedding(search_term)
 
         Discourse
           .cache
-          .fetch(embedding_key, expires_in: 1.week) { vector_rep.vector_from(hypothetical_post) }
+          .fetch(embedding_key, expires_in: 1.week) { vector.vector_from(hypothetical_post) }
       end
 
       def embedding(search_term)
         digest = OpenSSL::Digest::SHA1.hexdigest(search_term)
         embedding_key = build_embedding_key(digest, "", SiteSetting.ai_embeddings_model)
 
-        Discourse
-          .cache
-          .fetch(embedding_key, expires_in: 1.week) { vector_rep.vector_from(search_term) }
+        Discourse.cache.fetch(embedding_key, expires_in: 1.week) { vector.vector_from(search_term) }
       end
 
       # this ensures the candidate topics are over selected
@@ -84,7 +82,7 @@ def search_for_topics(query, page = 1, hyde: true)
 
         over_selection_limit = limit * OVER_SELECTION_FACTOR
 
-        schema = DiscourseAi::Embeddings::Schema.for(Topic, vector: vector_rep)
+        schema = DiscourseAi::Embeddings::Schema.for(Topic, vector_def: vector.vdef)
 
         candidate_topic_ids =
           schema.asymmetric_similarity_search(
@@ -114,7 +112,7 @@ def quick_search(query)
 
         return [] if search_term.nil? || search_term.length < SiteSetting.min_search_term_length
 
-        vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation
+        vector = DiscourseAi::Embeddings::Vector.instance
 
         digest = OpenSSL::Digest::SHA1.hexdigest(search_term)
 
@@ -129,12 +127,12 @@ def quick_search(query)
           Discourse
             .cache
             .fetch(embedding_key, expires_in: 1.week) do
-              vector_rep.vector_from(search_term, asymetric: true)
+              vector.vector_from(search_term, asymetric: true)
             end
 
         candidate_post_ids =
           DiscourseAi::Embeddings::Schema
-            .for(Post, vector: vector_rep)
+            .for(Post, vector_def: vector.vdef)
             .asymmetric_similarity_search(
               search_term_embedding,
               limit: max_semantic_results_per_page,

lib/embeddings/strategies/truncation.rb

@@ -12,19 +12,28 @@ def version
           1
         end
 
-        def prepare_text_from(target, tokenizer, max_length)
+        def prepare_target_text(target, vdef)
+          max_length = vdef.max_sequence_length - 2
+
           case target
           when Topic
-            topic_truncation(target, tokenizer, max_length)
+            topic_truncation(target, vdef.tokenizer, max_length)
           when Post
-            post_truncation(target, tokenizer, max_length)
+            post_truncation(target, vdef.tokenizer, max_length)
           when RagDocumentFragment
-            tokenizer.truncate(target.fragment, max_length)
+            vdef.tokenizer.truncate(target.fragment, max_length)
           else
             raise ArgumentError, "Invalid target type"
           end
         end
 
+        def prepare_query_text(text, vdef, asymetric: false)
+          qtext = asymetric ? "#{vdef.asymmetric_query_prefix} #{text}" : text
+          max_length = vdef.max_sequence_length - 2
+
+          vdef.tokenizer.truncate(text, max_length)
+        end
+
         private
 
         def topic_information(topic)