FEATURE: Per post embeddings (#387)

app/jobs/regular/generate_embeddings.rb

@@ -6,18 +6,20 @@ module Jobs
 
     def execute(args)
       return unless SiteSetting.ai_embeddings_enabled
-      return if (topic_id = args[:topic_id]).blank?
+      return if args[:target_type].blank? || args[:target_id].blank?
+      target = args[:target_type].constantize.find_by_id(args[:target_id])
+      return if target.nil? || target.deleted_at.present?
 
-      topic = Topic.find_by_id(topic_id)
-      return if topic.nil? || topic.private_message? && !SiteSetting.ai_embeddings_generate_for_pms
-      post = topic.first_post
-      return if post.nil? || post.raw.blank?
+      topic = target.is_a?(Topic) ? target : target.topic
+      post = target.is_a?(Post) ? target : target.first_post
+      return if topic.private_message? && !SiteSetting.ai_embeddings_generate_for_pms
+      return if post.raw.blank?
 
       strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
       vector_rep =
         DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
 
-      vector_rep.generate_topic_representation_from(topic)
+      vector_rep.generate_representation_from(target)
     end
   end
 end

app/jobs/scheduled/embeddings_backfill.rb

@@ -15,7 +15,7 @@ module Jobs
       strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
       vector_rep =
         DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
-      table_name = vector_rep.table_name
+      table_name = vector_rep.topic_table_name
 
       topics =
         Topic
@@ -28,7 +28,7 @@ module Jobs
       topics
         .where("#{table_name}.topic_id IS NULL")
         .find_each do |t|
-          vector_rep.generate_topic_representation_from(t)
+          vector_rep.generate_representation_from(t)
           rebaked += 1
         end
 
@@ -45,7 +45,7 @@ module Jobs
           #{table_name}.strategy_version < #{strategy.version}
         SQL
         .find_each do |t|
-          vector_rep.generate_topic_representation_from(t)
+          vector_rep.generate_representation_from(t)
           rebaked += 1
         end
 
@@ -59,7 +59,57 @@ module Jobs
         .limit((limit - rebaked) / 10)
         .pluck(:id)
         .each do |id|
-          vector_rep.generate_topic_representation_from(Topic.find_by(id: id))
+          vector_rep.generate_representation_from(Topic.find_by(id: id))
+          rebaked += 1
+        end
+
+      return if rebaked >= limit
+
+      # Now for posts
+      table_name = vector_rep.post_table_name
+
+      posts =
+        Post
+          .joins("LEFT JOIN #{table_name} ON #{table_name}.post_id = posts.id")
+          .where(deleted_at: nil)
+          .limit(limit - rebaked)
+
+      # First, we'll try to backfill embeddings for posts that have none
+      posts
+        .where("#{table_name}.post_id IS NULL")
+        .find_each do |t|
+          vector_rep.generate_representation_from(t)
+          rebaked += 1
+        end
+
+      vector_rep.consider_indexing
+
+      return if rebaked >= limit
+
+      # Then, we'll try to backfill embeddings for posts that have outdated
+      # embeddings, be it model or strategy version
+      posts
+        .where(<<~SQL)
+          #{table_name}.model_version < #{vector_rep.version}
+          OR
+          #{table_name}.strategy_version < #{strategy.version}
+        SQL
+        .find_each do |t|
+          vector_rep.generate_representation_from(t)
+          rebaked += 1
+        end
+
+      return if rebaked >= limit
+
+      # Finally, we'll try to backfill embeddings for posts that have outdated
+      # embeddings due to edits. Here we only do 10% of the limit
+      posts
+        .where("#{table_name}.updated_at < ?", 7.days.ago)
+        .order("random()")
+        .limit((limit - rebaked) / 10)
+        .pluck(:id)
+        .each do |id|
+          vector_rep.generate_representation_from(Post.find_by(id: id))
           rebaked += 1
         end
 

db/migrate/20230710171143_migrate_embeddings_from_dedicated_database.rb

@@ -14,7 +14,7 @@ class MigrateEmbeddingsFromDedicatedDatabase < ActiveRecord::Migration[7.0]
       ].map { |k| k.new(truncation) }
 
     vector_reps.each do |vector_rep|
-      new_table_name = vector_rep.table_name
+      new_table_name = vector_rep.topic_table_name
       old_table_name = "topic_embeddings_#{vector_rep.name.underscore}"
 
       begin

db/migrate/20231228213036_create_ai_post_embeddings_tables.rb

@@ -0,0 +1,60 @@
+# frozen_string_literal: true
+
+class CreateAiPostEmbeddingsTables < ActiveRecord::Migration[7.0]
+  def change
+    create_table :ai_post_embeddings_1_1, id: false do |t|
+      t.integer :post_id, null: false
+      t.integer :model_version, null: false
+      t.integer :strategy_version, null: false
+      t.text :digest, null: false
+      t.column :embeddings, "vector(768)", null: false
+      t.timestamps
+
+      t.index :post_id, unique: true
+    end
+
+    create_table :ai_post_embeddings_2_1, id: false do |t|
+      t.integer :post_id, null: false
+      t.integer :model_version, null: false
+      t.integer :strategy_version, null: false
+      t.text :digest, null: false
+      t.column :embeddings, "vector(1536)", null: false
+      t.timestamps
+
+      t.index :post_id, unique: true
+    end
+
+    create_table :ai_post_embeddings_3_1, id: false do |t|
+      t.integer :post_id, null: false
+      t.integer :model_version, null: false
+      t.integer :strategy_version, null: false
+      t.text :digest, null: false
+      t.column :embeddings, "vector(1024)", null: false
+      t.timestamps
+
+      t.index :post_id, unique: true
+    end
+
+    create_table :ai_post_embeddings_4_1, id: false do |t|
+      t.integer :post_id, null: false
+      t.integer :model_version, null: false
+      t.integer :strategy_version, null: false
+      t.text :digest, null: false
+      t.column :embeddings, "vector(1024)", null: false
+      t.timestamps
+
+      t.index :post_id, unique: true
+    end
+
+    create_table :ai_post_embeddings_5_1, id: false do |t|
+      t.integer :post_id, null: false
+      t.integer :model_version, null: false
+      t.integer :strategy_version, null: false
+      t.text :digest, null: false
+      t.column :embeddings, "vector(768)", null: false
+      t.timestamps
+
+      t.index :post_id, unique: true
+    end
+  end
+end

lib/embeddings/entry_point.rb

@@ -43,14 +43,20 @@ module DiscourseAi
 
         # embeddings generation.
         callback =
-          Proc.new do |topic|
+          Proc.new do |target|
             if SiteSetting.ai_embeddings_enabled
-              Jobs.enqueue(:generate_embeddings, topic_id: topic.id)
+              Jobs.enqueue(
+                :generate_embeddings,
+                target_id: target.id,
+                target_type: target.class.name,
+              )
             end
           end
 
         plugin.on(:topic_created, &callback)
         plugin.on(:topic_edited, &callback)
+        plugin.on(:post_created, &callback)
+        plugin.on(:post_edited, &callback)
       end
     end
   end

lib/embeddings/strategies/truncation.rb

@@ -50,9 +50,9 @@ module DiscourseAi
           tokenizer.truncate(text, max_length)
         end
 
-        def post_truncation(topic, tokenizer, max_length)
+        def post_truncation(post, tokenizer, max_length)
           text = +topic_information(post.topic)
-          text << post.raw
+          text << Nokogiri::HTML5.fragment(post.cooked).text
 
           tokenizer.truncate(text, max_length)
         end

lib/embeddings/vector_representations/base.rb

@@ -21,6 +21,8 @@ module DiscourseAi
         end
 
         def consider_indexing(memory: "100MB")
+          [topic_table_name, post_table_name].each do |table_name|
+            index_name = index_name(table_name)
             # Using extension maintainer's recommendation for ivfflat indexes
             # Results are not as good as without indexes, but it's much faster
             # Disk usage is ~1x the size of the table, so this doubles table total size
@@ -40,7 +42,7 @@ module DiscourseAi
 
             if !existing_index.present?
               Rails.logger.info("Index #{index_name} does not exist, creating...")
-            return create_index!(memory, lists, probes)
+              return create_index!(table_name, memory, lists, probes)
             end
 
             existing_index_age =
@@ -62,12 +64,12 @@ module DiscourseAi
                 Rails.logger.info(
                   "Index #{index_name} is #{existing_index_age} seconds old, and there are #{new_rows} new rows, updating...",
                 )
-              return create_index!(memory, lists, probes)
+                return create_index!(table_name, memory, lists, probes)
               elsif existing_lists != lists
                 Rails.logger.info(
                   "Index #{index_name} already exists, but lists is #{existing_lists} instead of #{lists}, updating...",
                 )
-              return create_index!(memory, lists, probes)
+                return create_index!(table_name, memory, lists, probes)
               end
             end
 
@@ -75,8 +77,10 @@ module DiscourseAi
               "Index #{index_name} kept. #{Time.now.to_i - existing_index_age} seconds old, #{new_rows} new rows, #{existing_lists} lists, #{probes} probes.",
             )
           end
+        end
 
-        def create_index!(memory, lists, probes)
+        def create_index!(table_name, memory, lists, probes)
+          index_name = index_name(table_name)
           DB.exec("SET work_mem TO '#{memory}';")
           DB.exec("SET maintenance_work_mem TO '#{memory}';")
           DB.exec(<<~SQL)
@@ -102,17 +106,17 @@ module DiscourseAi
           raise NotImplementedError
         end
 
-        def generate_topic_representation_from(target, persist: true)
+        def generate_representation_from(target, persist: true)
           text = @strategy.prepare_text_from(target, tokenizer, max_sequence_length - 2)
 
           new_digest = OpenSSL::Digest::SHA1.hexdigest(text)
-          current_digest = DB.query_single(<<~SQL, topic_id: target.id).first
+          current_digest = DB.query_single(<<~SQL, target_id: target.id).first
             SELECT
               digest
             FROM
-              #{table_name}
+              #{table_name(target)}
             WHERE
-              topic_id = :topic_id
+              #{target.is_a?(Topic) ? "topic_id" : "post_id"} = :target_id
             LIMIT 1
           SQL
           return if current_digest == new_digest
@@ -127,7 +131,19 @@ module DiscourseAi
             SELECT
               topic_id
             FROM
-              #{table_name}
+              #{topic_table_name}
+            ORDER BY
+              embeddings #{pg_function} '[:query_embedding]'
+            LIMIT 1
+          SQL
+        end
+
+        def post_id_from_representation(raw_vector)
+          DB.query_single(<<~SQL, query_embedding: raw_vector).first
+            SELECT
+              post_id
+            FROM
+              #{post_table_name}
             ORDER BY
               embeddings #{pg_function} '[:query_embedding]'
             LIMIT 1
@@ -140,7 +156,7 @@ module DiscourseAi
               topic_id,
               embeddings #{pg_function} '[:query_embedding]' AS distance
             FROM
-              #{table_name}
+              #{topic_table_name}
             ORDER BY
               embeddings #{pg_function} '[:query_embedding]'
             LIMIT :limit
@@ -162,13 +178,13 @@ module DiscourseAi
             SELECT
               topic_id
             FROM
-              #{table_name}
+              #{topic_table_name}
             ORDER BY
               embeddings #{pg_function} (
                 SELECT
                   embeddings
                 FROM
-                  #{table_name}
+                  #{topic_table_name}
                 WHERE
                   topic_id = :topic_id
                 LIMIT 1
@@ -182,11 +198,26 @@ module DiscourseAi
           raise MissingEmbeddingError
         end
 
-        def table_name
+        def topic_table_name
           "ai_topic_embeddings_#{id}_#{@strategy.id}"
         end
 
-        def index_name
+        def post_table_name
+          "ai_post_embeddings_#{id}_#{@strategy.id}"
+        end
+
+        def table_name(target)
+          case target
+          when Topic
+            topic_table_name
+          when Post
+            post_table_name
+          else
+            raise ArgumentError, "Invalid target type"
+          end
+        end
+
+        def index_name(table_name)
           "#{table_name}_search"
         end
 
@@ -221,9 +252,10 @@ module DiscourseAi
         protected
 
         def save_to_db(target, vector, digest)
+          if target.is_a?(Topic)
             DB.exec(
               <<~SQL,
-            INSERT INTO #{table_name} (topic_id, model_version, strategy_version, digest, embeddings, created_at, updated_at)
+              INSERT INTO #{topic_table_name} (topic_id, model_version, strategy_version, digest, embeddings, created_at, updated_at)
               VALUES (:topic_id, :model_version, :strategy_version, :digest, '[:embeddings]', CURRENT_TIMESTAMP, CURRENT_TIMESTAMP)
               ON CONFLICT (topic_id)
               DO UPDATE SET
@@ -239,6 +271,28 @@ module DiscourseAi
               digest: digest,
               embeddings: vector,
             )
+          elsif target.is_a?(Post)
+            DB.exec(
+              <<~SQL,
+              INSERT INTO #{post_table_name} (post_id, model_version, strategy_version, digest, embeddings, created_at, updated_at)
+              VALUES (:post_id, :model_version, :strategy_version, :digest, '[:embeddings]', CURRENT_TIMESTAMP, CURRENT_TIMESTAMP)
+              ON CONFLICT (post_id)
+              DO UPDATE SET
+                model_version = :model_version,
+                strategy_version = :strategy_version,
+                digest = :digest,
+                embeddings = '[:embeddings]',
+                updated_at = CURRENT_TIMESTAMP
+              SQL
+              post_id: target.id,
+              model_version: version,
+              strategy_version: @strategy.version,
+              digest: digest,
+              embeddings: vector,
+            )
+          else
+            raise ArgumentError, "Invalid target type"
+          end
         end
       end
     end

lib/tasks/modules/embeddings/database.rake

@@ -1,23 +1,33 @@
 # frozen_string_literal: true
 
-desc "Backfill embeddings for all topics"
-task "ai:embeddings:backfill", [:start_topic] => [:environment] do |_, args|
+desc "Backfill embeddings for all topics and posts"
+task "ai:embeddings:backfill" => [:environment] do
   public_categories = Category.where(read_restricted: false).pluck(:id)
 
   strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
   vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
-  table_name = vector_rep.table_name
+  table_name = vector_rep.topic_table_name
 
   Topic
     .joins("LEFT JOIN #{table_name} ON #{table_name}.topic_id = topics.id")
     .where("#{table_name}.topic_id IS NULL")
-    .where("topics.id >= ?", args[:start_topic].to_i || 0)
     .where("category_id IN (?)", public_categories)
     .where(deleted_at: nil)
-    .order("topics.id ASC")
+    .order("topics.id DESC")
     .find_each do |t|
       print "."
-      vector_rep.generate_topic_representation_from(t)
+      vector_rep.generate_representation_from(t)
+    end
+
+  table_name = vector_rep.post_table_name
+  Post
+    .joins("LEFT JOIN #{table_name} ON #{table_name}.post_id = posts.id")
+    .where("#{table_name}.post_id IS NULL")
+    .where(deleted_at: nil)
+    .order("posts.id DESC")
+    .find_each do |t|
+      print "."
+      vector_rep.generate_representation_from(t)
     end
 end
 

spec/lib/modules/embeddings/entry_point_spec.rb

@@ -18,7 +18,7 @@ describe DiscourseAi::Embeddings::EntryPoint do
       it "queues a job on create if embeddings is enabled" do
         SiteSetting.ai_embeddings_enabled = true
 
-        expect { creator.create }.to change(Jobs::GenerateEmbeddings.jobs, :size).by(1)
+        expect { creator.create }.to change(Jobs::GenerateEmbeddings.jobs, :size).by(2) # topic_created and post_created
       end
 
       it "does nothing if sentiment analysis is disabled" do

spec/lib/modules/embeddings/jobs/generate_embeddings_spec.rb

@@ -18,7 +18,7 @@ RSpec.describe Jobs::GenerateEmbeddings do
       DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(truncation)
     end
 
-    it "works" do
+    it "works for topics" do
       expected_embedding = [0.0038493] * vector_rep.dimensions
 
       text =
@@ -29,9 +29,21 @@ RSpec.describe Jobs::GenerateEmbeddings do
         )
       EmbeddingsGenerationStubs.discourse_service(vector_rep.name, text, expected_embedding)
 
-      job.execute(topic_id: topic.id)
+      job.execute(target_id: topic.id, target_type: "Topic")
 
       expect(vector_rep.topic_id_from_representation(expected_embedding)).to eq(topic.id)
     end
+
+    it "works for posts" do
+      expected_embedding = [0.0038493] * vector_rep.dimensions
+
+      text =
+        truncation.prepare_text_from(post, vector_rep.tokenizer, vector_rep.max_sequence_length - 2)
+      EmbeddingsGenerationStubs.discourse_service(vector_rep.name, text, expected_embedding)
+
+      job.execute(target_id: post.id, target_type: "Post")
+
+      expect(vector_rep.post_id_from_representation(expected_embedding)).to eq(post.id)
+    end
   end
 end

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

@@ -12,9 +12,10 @@ RSpec.shared_examples "generates and store embedding using with vector represent
     end
   end
 
-  describe "#generate_topic_representation_from" do
+  describe "#generate_representation_from" do
     fab!(:topic) { Fabricate(:topic) }
     fab!(:post) { Fabricate(:post, post_number: 1, topic: topic) }
+    fab!(:post2) { Fabricate(:post, post_number: 2, topic: topic) }
 
     it "creates a vector from a topic and stores it in the database" do
       text =
@@ -25,10 +26,24 @@ RSpec.shared_examples "generates and store embedding using with vector represent
         )
       stub_vector_mapping(text, @expected_embedding)
 
-      vector_rep.generate_topic_representation_from(topic)
+      vector_rep.generate_representation_from(topic)
 
       expect(vector_rep.topic_id_from_representation(@expected_embedding)).to eq(topic.id)
     end
+
+    it "creates a vector from a post and stores it in the database" do
+      text =
+        truncation.prepare_text_from(
+          post2,
+          vector_rep.tokenizer,
+          vector_rep.max_sequence_length - 2,
+        )
+      stub_vector_mapping(text, @expected_embedding)
+
+      vector_rep.generate_representation_from(post)
+
+      expect(vector_rep.post_id_from_representation(@expected_embedding)).to eq(post.id)
+    end
   end
 
   describe "#asymmetric_topics_similarity_search" do
@@ -44,7 +59,7 @@ RSpec.shared_examples "generates and store embedding using with vector represent
           vector_rep.max_sequence_length - 2,
         )
       stub_vector_mapping(text, @expected_embedding)
-      vector_rep.generate_topic_representation_from(topic)
+      vector_rep.generate_representation_from(topic)
 
       expect(
         vector_rep.asymmetric_topics_similarity_search(similar_vector, limit: 1, offset: 0),