FIX: Restore the accidentally deleted query prefix.

Additionally, we add a prefix for embedding generation.
Both are stored in the definitions table.

Author: romanrizzi

app/controllers/discourse_ai/admin/ai_embeddings_controller.rb

@@ -111,6 +111,8 @@ def ai_embeddings_params
             :url,
             :api_key,
             :tokenizer_class,
+            :embed_prompt,
+            :search_prompt,
           )
 
         extra_field_names = EmbeddingDefinition.provider_params.dig(permitted[:provider]&.to_sym)

app/models/embedding_definition.rb

@@ -42,6 +42,7 @@ def presets
               pg_function: "<#>",
               tokenizer_class: "DiscourseAi::Tokenizer::BgeLargeEnTokenizer",
               provider: HUGGING_FACE,
+              search_prompt: "Represent this sentence for searching relevant passages:",
             },
             {
               preset_id: "bge-m3",
@@ -228,4 +229,6 @@ def gemini_client
 #  provider_params     :jsonb
 #  created_at          :datetime         not null
 #  updated_at          :datetime         not null
+#  embed_prompt        :string           default(""), not null
+#  search_prompt       :string           default(""), not null
 #

app/serializers/ai_embedding_definition_serializer.rb

@@ -13,6 +13,8 @@ class AiEmbeddingDefinitionSerializer < ApplicationSerializer
              :api_key,
              :seeded,
              :tokenizer_class,
+             :embed_prompt,
+             :search_prompt,
              :provider_params
 
   def api_key

assets/javascripts/discourse/admin/models/ai-embedding.js

@@ -14,7 +14,9 @@ export default class AiEmbedding extends RestModel {
       "api_key",
       "max_sequence_length",
       "provider_params",
-      "pg_function"
+      "pg_function",
+      "embed_prompt",
+      "search_prompt"
     );
   }
 

assets/javascripts/discourse/components/ai-embedding-editor.gjs

@@ -290,6 +290,24 @@ export default class AiEmbeddingEditor extends Component {
           {{/if}}
         </div>
 
+        <div class="control-group">
+          <label>{{i18n "discourse_ai.embeddings.embed_prompt"}}</label>
+          <Input
+            @type="text"
+            class="ai-embedding-editor-input ai-embedding-editor__embed_prompt"
+            @value={{this.editingModel.embed_prompt}}
+          />
+        </div>
+
+        <div class="control-group">
+          <label>{{i18n "discourse_ai.embeddings.search_prompt"}}</label>
+          <Input
+            @type="text"
+            class="ai-embedding-editor-input ai-embedding-editor__search_prompt"
+            @value={{this.editingModel.search_prompt}}
+          />
+        </div>
+
         <div class="control-group">
           <label>{{i18n "discourse_ai.embeddings.max_sequence_length"}}</label>
           <Input

config/locales/client.en.yml

@@ -530,7 +530,9 @@ en:
         tokenizer: "Tokenizer"
         dimensions: "Embedding dimensions"
         max_sequence_length: "Sequence length"
-        
+        embed_prompt: "Embed prompt"
+        search_prompt: "Search prompt"
+
         distance_function: "Distance function"
         distance_functions:
           <#>: "Negative inner product (<#>)"

db/migrate/20250121162520_configurable_embeddings_prefixes.rb

@@ -0,0 +1,18 @@
+# frozen_string_literal: true
+class ConfigurableEmbeddingsPrefixes < ActiveRecord::Migration[7.2]
+  def up
+    add_column :embedding_definitions, :embed_prompt, :string, null: false, default: ""
+    add_column :embedding_definitions, :search_prompt, :string, null: false, default: ""
+
+    # 4 is bge-large-en. Default model and the only one using this so far.
+    execute <<~SQL
+      UPDATE embedding_definitions
+      SET search_prompt='Represent this sentence for searching relevant passages:'
+      WHERE id = 4
+    SQL
+  end
+
+  def down
+    raise ActiveRecord::IrreversibleMigration
+  end
+end

lib/embeddings/strategies/truncation.rb

@@ -15,23 +15,28 @@ def version
         def prepare_target_text(target, vdef)
           max_length = vdef.max_sequence_length - 2
 
-          case target
-          when Topic
-            topic_truncation(target, vdef.tokenizer, max_length)
-          when Post
-            post_truncation(target, vdef.tokenizer, max_length)
-          when RagDocumentFragment
-            vdef.tokenizer.truncate(target.fragment, max_length)
-          else
-            raise ArgumentError, "Invalid target type"
-          end
+          prepared_text =
+            case target
+            when Topic
+              topic_truncation(target, vdef.tokenizer, max_length)
+            when Post
+              post_truncation(target, vdef.tokenizer, max_length)
+            when RagDocumentFragment
+              vdef.tokenizer.truncate(target.fragment, max_length)
+            else
+              raise ArgumentError, "Invalid target type"
+            end
+
+          return prepared_text if vdef.embed_prompt.blank?
+
+          [vdef.embed_prompt, prepared_text].join(" ")
         end
 
         def prepare_query_text(text, vdef, asymetric: false)
-          qtext = asymetric ? "#{vdef.asymmetric_query_prefix} #{text}" : text
+          qtext = asymetric ? "#{vdef.search_prompt} #{text}" : text
           max_length = vdef.max_sequence_length - 2
 
-          vdef.tokenizer.truncate(text, max_length)
+          vdef.tokenizer.truncate(qtext, max_length)
         end
 
         private

spec/lib/modules/embeddings/strategies/truncation_spec.rb

@@ -3,29 +3,51 @@
 RSpec.describe DiscourseAi::Embeddings::Strategies::Truncation do
   subject(:truncation) { described_class.new }
 
-  describe "#prepare_query_text" do
-    context "when using vector def from OpenAI" do
-      before { SiteSetting.max_post_length = 100_000 }
+  fab!(:open_ai_embedding_def)
+  let(:prefix) { "I come first:" }
 
-      fab!(:topic)
-      fab!(:post) do
-        Fabricate(:post, topic: topic, raw: "Baby, bird, bird, bird\nBird is the word\n" * 500)
-      end
-      fab!(:post) do
-        Fabricate(
-          :post,
-          topic: topic,
-          raw: "Don't you know about the bird?\nEverybody knows that the bird is a word\n" * 400,
-        )
-      end
-      fab!(:post) { Fabricate(:post, topic: topic, raw: "Surfin' bird\n" * 800) }
-      fab!(:open_ai_embedding_def)
+  describe "#prepare_target_text" do
+    before { SiteSetting.max_post_length = 100_000 }
+
+    fab!(:topic)
+    fab!(:post) do
+      Fabricate(:post, topic: topic, raw: "Baby, bird, bird, bird\nBird is the word\n" * 500)
+    end
+    fab!(:post) do
+      Fabricate(
+        :post,
+        topic: topic,
+        raw: "Don't you know about the bird?\nEverybody knows that the bird is a word\n" * 400,
+      )
+    end
+    fab!(:post) { Fabricate(:post, topic: topic, raw: "Surfin' bird\n" * 800) }
+    fab!(:open_ai_embedding_def)
+
+    it "truncates a topic" do
+      prepared_text = truncation.prepare_target_text(topic, open_ai_embedding_def)
+
+      expect(open_ai_embedding_def.tokenizer.size(prepared_text)).to be <=
+        open_ai_embedding_def.max_sequence_length
+    end
+
+    it "includes embed prefix" do
+      open_ai_embedding_def.update!(embed_prompt: prefix)
+
+      prepared_text = truncation.prepare_target_text(topic, open_ai_embedding_def)
+
+      expect(prepared_text.starts_with?(prefix)).to eq(true)
+    end
+  end
+
+  describe "#prepare_query_text" do
+    context "when search is asymetric" do
+      it "includes search prefix" do
+        open_ai_embedding_def.update!(search_prompt: prefix)
 
-      it "truncates a topic" do
-        prepared_text = truncation.prepare_target_text(topic, open_ai_embedding_def)
+        prepared_query_text =
+          truncation.prepare_query_text("searching", open_ai_embedding_def, asymetric: true)
 
-        expect(open_ai_embedding_def.tokenizer.size(prepared_text)).to be <=
-          open_ai_embedding_def.max_sequence_length
+        expect(prepared_query_text.starts_with?(prefix)).to eq(true)
       end
     end
   end

spec/requests/admin/ai_embeddings_controller_spec.rb

@@ -15,6 +15,8 @@
       url: "https://test.com/api/v1/embeddings",
       api_key: "test",
       tokenizer_class: "DiscourseAi::Tokenizer::BgeM3Tokenizer",
+      embed_prompt: "I come first:",
+      search_prompt: "prefix for search",
     }
   end
 
@@ -27,6 +29,8 @@
 
         expect(response.status).to eq(201)
         expect(created_def.display_name).to eq(valid_attrs[:display_name])
+        expect(created_def.embed_prompt).to eq(valid_attrs[:embed_prompt])
+        expect(created_def.search_prompt).to eq(valid_attrs[:search_prompt])
       end
 
       it "stores provider-specific config params" do