FEATURE: add semantic search with hyde bot (#210)

In specific scenarios (no special filters or limits) we will also
always include 5 semantic results (at least) with every query.

This effectively means that all very wide queries will always return
20 results, regardless of how complex they are.

Also: 

FIX: embedding backfill rake task not working
We renamed internals, this corrects the implementation

lib/modules/ai_bot/commands/search_command.rb

@@ -15,7 +15,8 @@ module DiscourseAi::AiBot::Commands
         [
           Parameter.new(
             name: "search_query",
-            description: "Search query (correct bad spelling, remove connector words!)",
+            description:
+              "Specific keywords to search for, space seperated (correct bad spelling, remove connector words)",
             type: "string",
           ),
           Parameter.new(
@@ -93,6 +94,9 @@ module DiscourseAi::AiBot::Commands
       }
     end
 
+    MAX_RESULTS = 20
+    MIN_SEMANTIC_RESULTS = 5
+
     def process(**search_args)
       limit = nil
 
@@ -120,12 +124,35 @@ module DiscourseAi::AiBot::Commands
         )
 
       # let's be frugal with tokens, 50 results is too much and stuff gets cut off
-      limit ||= 20
+      limit ||= MAX_RESULTS
-      limit = 20 if limit > 20
+      limit = MAX_RESULTS if limit > MAX_RESULTS
+
+      should_try_semantic_search = SiteSetting.ai_embeddings_semantic_search_enabled
+      should_try_semantic_search &&= (limit == MAX_RESULTS)
+      should_try_semantic_search &&= (search_args.keys - %i[search_query order]).length == 0
+      should_try_semantic_search &&= (search_args[:search_query].present?)
+
+      limit = limit - MIN_SEMANTIC_RESULTS if should_try_semantic_search
 
       posts = results&.posts || []
       posts = posts[0..limit - 1]
 
+      if should_try_semantic_search
+        semantic_search = DiscourseAi::Embeddings::SemanticSearch.new(Guardian.new())
+        topic_ids = Set.new(posts.map(&:topic_id))
+
+        semantic_search
+          .search_for_topics(search_args[:search_query])
+          .each do |post|
+            next if topic_ids.include?(post.topic_id)
+
+            topic_ids << post.topic_id
+            posts << post
+
+            break if posts.length >= MAX_RESULTS
+          end
+      end
+
       @last_num_results = posts.length
 
       if posts.blank?

lib/tasks/modules/embeddings/database.rake

@@ -3,11 +3,9 @@
 desc "Backfill embeddings for all topics"
 task "ai:embeddings:backfill", [:start_topic] => [:environment] do |_, args|
   public_categories = Category.where(read_restricted: false).pluck(:id)
-  manager = DiscourseAi::Embeddings::Manager.new(Topic.first)
 
   strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
-  vector_rep =
+  vector_rep = DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
-    DiscourseAi::Embeddings::VectorRepresentations::Base.find_vector_representation.new(strategy)
   table_name = vector_rep.table_name
 
   Topic

spec/lib/modules/ai_bot/commands/search_command_spec.rb

@@ -1,6 +1,7 @@
 #frozen_string_literal: true
 
 require_relative "../../../../support/openai_completions_inference_stubs"
+require_relative "../../../../support/embeddings_generation_stubs"
 
 RSpec.describe DiscourseAi::AiBot::Commands::SearchCommand do
   let(:bot_user) { User.find(DiscourseAi::AiBot::EntryPoint::GPT3_5_TURBO_ID) }
@@ -19,6 +20,43 @@ RSpec.describe DiscourseAi::AiBot::Commands::SearchCommand do
       expect(results[:rows]).to eq([])
     end
 
+    describe "semantic search" do
+      let (:query) {
+        "this is an expanded search"
+      }
+      after { DiscourseAi::Embeddings::SemanticSearch.clear_cache_for(query) }
+
+      it "supports semantic search when enabled" do
+        SiteSetting.ai_embeddings_semantic_search_enabled = true
+        SiteSetting.ai_embeddings_discourse_service_api_endpoint = "http://test.com"
+
+        WebMock.stub_request(:post, "https://api.openai.com/v1/chat/completions").to_return(
+          status: 200,
+          body: JSON.dump(OpenAiCompletionsInferenceStubs.response(query)),
+        )
+
+        hyde_embedding = [0.049382, 0.9999]
+        EmbeddingsGenerationStubs.discourse_service(
+          SiteSetting.ai_embeddings_model,
+          query,
+          hyde_embedding,
+        )
+
+        post1 = Fabricate(:post)
+        search = described_class.new(bot_user: bot_user, post: post1, args: nil)
+
+        DiscourseAi::Embeddings::VectorRepresentations::AllMpnetBaseV2
+          .any_instance
+          .expects(:asymmetric_topics_similarity_search)
+          .returns([post1.topic_id])
+
+        results = search.process(search_query: "hello world, sam")
+
+        expect(results[:args]).to eq({ search_query: "hello world, sam" })
+        expect(results[:rows].length).to eq(1)
+      end
+    end
+
     it "supports subfolder properly" do
       Discourse.stubs(:base_path).returns("/subfolder")