REFACTOR: Tidy-up embedding endpoints config.

lib/embeddings/vector_representations/all_mpnet_base_v2.rb

@@ -24,12 +24,7 @@ def dependant_setting_names
         end
 
         def vector_from(text, asymetric: false)
-          DiscourseAi::Inference::DiscourseClassifier.perform!(
-            "#{discourse_embeddings_endpoint}/api/v1/classify",
-            self.class.name,
-            text,
-            SiteSetting.ai_embeddings_discourse_service_api_key,
-          )
+          inference_client.perform!(text)
         end
 
         def dimensions
@@ -59,6 +54,10 @@ def pg_index_type
         def tokenizer
           DiscourseAi::Tokenizer::AllMpnetBaseV2Tokenizer
         end
+
+        def inference_client
+          DiscourseAi::Inference::DiscourseClassifier.instance(self.class.name)
+        end
       end
     end
   end

lib/embeddings/vector_representations/base.rb

@@ -426,16 +426,8 @@ def save_to_db(target, vector, digest)
           end
         end
 
-        def discourse_embeddings_endpoint
-          if SiteSetting.ai_embeddings_discourse_service_api_endpoint_srv.present?
-            service =
-              DiscourseAi::Utils::DnsSrv.lookup(
-                SiteSetting.ai_embeddings_discourse_service_api_endpoint_srv,
-              )
-            "https://#{service.target}:#{service.port}"
-          else
-            SiteSetting.ai_embeddings_discourse_service_api_endpoint
-          end
+        def inference_client
+          raise NotImplementedError
         end
       end
     end

lib/embeddings/vector_representations/bge_large_en.rb

@@ -33,24 +33,12 @@ def dependant_setting_names
         def vector_from(text, asymetric: false)
           text = "#{asymmetric_query_prefix} #{text}" if asymetric
 
-          if SiteSetting.ai_cloudflare_workers_api_token.present?
-            DiscourseAi::Inference::CloudflareWorkersAi
-              .perform!(inference_model_name, { text: text })
-              .dig(:result, :data)
-              .first
-          elsif DiscourseAi::Inference::HuggingFaceTextEmbeddings.configured?
-            truncated_text = tokenizer.truncate(text, max_sequence_length - 2)
-            DiscourseAi::Inference::HuggingFaceTextEmbeddings.perform!(truncated_text).first
-          elsif discourse_embeddings_endpoint.present?
-            DiscourseAi::Inference::DiscourseClassifier.perform!(
-              "#{discourse_embeddings_endpoint}/api/v1/classify",
-              inference_model_name.split("/").last,
-              text,
-              SiteSetting.ai_embeddings_discourse_service_api_key,
-            )
-          else
-            raise "No inference endpoint configured"
-          end
+          client = inference_client
+
+          needs_truncation = client.class.name.include?("HuggingFaceTextEmbeddings")
+          text = tokenizer.truncate(text, max_sequence_length - 2) if needs_truncation
+
+          inference_client.perform!(text)
         end
 
         def inference_model_name
@@ -88,6 +76,21 @@ def tokenizer
         def asymmetric_query_prefix
           "Represent this sentence for searching relevant passages:"
         end
+
+        def inference_client
+          if SiteSetting.ai_cloudflare_workers_api_token.present?
+            DiscourseAi::Inference::CloudflareWorkersAi.instance(inference_model_name)
+          elsif DiscourseAi::Inference::HuggingFaceTextEmbeddings.configured?
+            DiscourseAi::Inference::HuggingFaceTextEmbeddings.instance
+          elsif SiteSetting.ai_embeddings_discourse_service_api_endpoint_srv.present? ||
+                SiteSetting.ai_embeddings_discourse_service_api_endpoint.present?
+            DiscourseAi::Inference::DiscourseClassifier.instance(
+              inference_model_name.split("/").last,
+            )
+          else
+            raise "No inference endpoint configured"
+          end
+        end
       end
     end
   end

lib/embeddings/vector_representations/bge_m3.rb

@@ -20,7 +20,7 @@ def dependant_setting_names
 
         def vector_from(text, asymetric: false)
           truncated_text = tokenizer.truncate(text, max_sequence_length - 2)
-          DiscourseAi::Inference::HuggingFaceTextEmbeddings.perform!(truncated_text).first
+          inference_client.perform!(truncated_text)
         end
 
         def dimensions
@@ -50,6 +50,10 @@ def pg_index_type
         def tokenizer
           DiscourseAi::Tokenizer::BgeM3Tokenizer
         end
+
+        def inference_client
+          DiscourseAi::Inference::HuggingFaceTextEmbeddings.instance
+        end
       end
     end
   end

lib/embeddings/vector_representations/gemini.rb

@@ -43,8 +43,7 @@ def pg_index_type
         end
 
         def vector_from(text, asymetric: false)
-          response = DiscourseAi::Inference::GeminiEmbeddings.perform!(text)
-          response[:embedding][:values]
+          inference_client.perform!(text).dig(:embedding, :values)
         end
 
         # There is no public tokenizer for Gemini, and from the ones we already ship in the plugin
@@ -53,6 +52,10 @@ def vector_from(text, asymetric: false)
         def tokenizer
           DiscourseAi::Tokenizer::OpenAiTokenizer
         end
+
+        def inference_client
+          DiscourseAi::Inference::GeminiEmbeddings.instance
+        end
       end
     end
   end

lib/embeddings/vector_representations/multilingual_e5_large.rb

@@ -29,19 +29,16 @@ def dependant_setting_names
         end
 
         def vector_from(text, asymetric: false)
-          if DiscourseAi::Inference::HuggingFaceTextEmbeddings.configured?
-            truncated_text = tokenizer.truncate(text, max_sequence_length - 2)
-            DiscourseAi::Inference::HuggingFaceTextEmbeddings.perform!(truncated_text).first
-          elsif discourse_embeddings_endpoint.present?
-            DiscourseAi::Inference::DiscourseClassifier.perform!(
-              "#{discourse_embeddings_endpoint}/api/v1/classify",
-              self.class.name,
-              "query: #{text}",
-              SiteSetting.ai_embeddings_discourse_service_api_key,
-            )
+          client = inference_client
+
+          needs_truncation = client.class.name.include?("HuggingFaceTextEmbeddings")
+          if needs_truncation
+            text = tokenizer.truncate(text, max_sequence_length - 2)
           else
-            raise "No inference endpoint configured"
+            text = "query: #{text}"
           end
+
+          client.perform!(text)
         end
 
         def id
@@ -71,6 +68,17 @@ def pg_index_type
         def tokenizer
           DiscourseAi::Tokenizer::MultilingualE5LargeTokenizer
         end
+
+        def inference_client
+          if DiscourseAi::Inference::HuggingFaceTextEmbeddings.configured?
+            DiscourseAi::Inference::HuggingFaceTextEmbeddings.instance
+          elsif SiteSetting.ai_embeddings_discourse_service_api_endpoint_srv.present? ||
+                SiteSetting.ai_embeddings_discourse_service_api_endpoint.present?
+            DiscourseAi::Inference::DiscourseClassifier.instance(self.class.name)
+          else
+            raise "No inference endpoint configured"
+          end
+        end
       end
     end
   end

lib/embeddings/vector_representations/text_embedding_3_large.rb

@@ -45,18 +45,19 @@ def pg_index_type
         end
 
         def vector_from(text, asymetric: false)
-          response =
-            DiscourseAi::Inference::OpenAiEmbeddings.perform!(
-              text,
-              model: self.class.name,
-              dimensions: dimensions,
-            )
-          response[:data].first[:embedding]
+          inference_client.perform!(text)
         end
 
         def tokenizer
           DiscourseAi::Tokenizer::OpenAiTokenizer
         end
+
+        def inference_client
+          DiscourseAi::Inference::OpenAiEmbeddings.instance(
+            model: self.class.name,
+            dimensions: dimensions,
+          )
+        end
       end
     end
   end

lib/embeddings/vector_representations/text_embedding_3_small.rb

@@ -43,13 +43,16 @@ def pg_index_type
         end
 
         def vector_from(text, asymetric: false)
-          response = DiscourseAi::Inference::OpenAiEmbeddings.perform!(text, model: self.class.name)
-          response[:data].first[:embedding]
+          inference_client.perform!(text)
         end
 
         def tokenizer
           DiscourseAi::Tokenizer::OpenAiTokenizer
         end
+
+        def inference_client
+          DiscourseAi::Inference::OpenAiEmbeddings.instance(model: self.class.name)
+        end
       end
     end
   end

lib/embeddings/vector_representations/text_embedding_ada_002.rb

@@ -43,13 +43,16 @@ def pg_index_type
         end
 
         def vector_from(text, asymetric: false)
-          response = DiscourseAi::Inference::OpenAiEmbeddings.perform!(text, model: self.class.name)
-          response[:data].first[:embedding]
+          inference_client.perform!(text)
         end
 
         def tokenizer
           DiscourseAi::Tokenizer::OpenAiTokenizer
         end
+
+        def inference_client
+          DiscourseAi::Inference::OpenAiEmbeddings.instance(model: self.class.name)
+        end
       end
     end
   end

lib/inference/cloudflare_workers_ai.rb

@@ -3,25 +3,38 @@
 module ::DiscourseAi
   module Inference
     class CloudflareWorkersAi
-      def self.perform!(model, content)
-        headers = { "Referer" => Discourse.base_url, "Content-Type" => "application/json" }
+      def initialize(account_id, api_token, model, referer = Discourse.base_url)
+        @account_id = account_id
+        @api_token = api_token
+        @model = model
+        @referer = referer
+      end
+
+      def self.instance(model)
+        new(
+          SiteSetting.ai_cloudflare_workers_account_id,
+          SiteSetting.ai_cloudflare_workers_api_token,
+          model,
+        )
+      end
 
-        account_id = SiteSetting.ai_cloudflare_workers_account_id
-        token = SiteSetting.ai_cloudflare_workers_api_token
+      attr_reader :account_id, :api_token, :model, :referer
 
-        base_url = "https://api.cloudflare.com/client/v4/accounts/#{account_id}/ai/run/@cf/"
-        headers["Authorization"] = "Bearer #{token}"
+      def perform!(content)
+        headers = {
+          "Referer" => Discourse.base_url,
+          "Content-Type" => "application/json",
+          "Authorization" => "Bearer #{api_token}",
+        }
 
-        endpoint = "#{base_url}#{model}"
+        endpoint = "https://api.cloudflare.com/client/v4/accounts/#{account_id}/ai/run/@cf/#{model}"
 
         conn = Faraday.new { |f| f.adapter FinalDestination::FaradayAdapter }
         response = conn.post(endpoint, content.to_json, headers)
 
-        raise Net::HTTPBadResponse if ![200].include?(response.status)
-
         case response.status
         when 200
-          JSON.parse(response.body, symbolize_names: true)
+          JSON.parse(response.body, symbolize_names: true).dig(:result, :data).first
         when 429
           # TODO add a AdminDashboard Problem?
         else

lib/inference/discourse_classifier.rb

@@ -3,9 +3,36 @@
 module ::DiscourseAi
   module Inference
     class DiscourseClassifier
-      def self.perform!(endpoint, model, content, api_key)
-        headers = { "Referer" => Discourse.base_url, "Content-Type" => "application/json" }
+      def initialize(endpoint, api_key, model, referer = Discourse.base_url)
+        @endpoint = endpoint
+        @api_key = api_key
+        @model = model
+        @referer = referer
+      end
+
+      def self.instance(model)
+        endpoint =
+          if SiteSetting.ai_embeddings_discourse_service_api_endpoint_srv.present?
+            service =
+              DiscourseAi::Utils::DnsSrv.lookup(
+                SiteSetting.ai_embeddings_discourse_service_api_endpoint_srv,
+              )
+            "https://#{service.target}:#{service.port}"
+          else
+            SiteSetting.ai_embeddings_discourse_service_api_endpoint
+          end
+
+        new(
+          "#{endpoint}/api/v1/classify",
+          SiteSetting.ai_embeddings_discourse_service_api_key,
+          model,
+        )
+      end
+
+      attr_reader :endpoint, :api_key, :model, :referer
 
+      def perform!(content)
+        headers = { "Referer" => referer, "Content-Type" => "application/json" }
         headers["X-API-KEY"] = api_key if api_key.present?
 
         conn = Faraday.new { |f| f.adapter FinalDestination::FaradayAdapter }

lib/inference/gemini_embeddings.rb

@@ -3,12 +3,17 @@
 module ::DiscourseAi
   module Inference
     class GeminiEmbeddings
-      def self.perform!(content)
-        headers = { "Referer" => Discourse.base_url, "Content-Type" => "application/json" }
+      def initialize(api_key, referer = Discourse.base_url)
+        @api_key = api_key
+        @referer = referer
+      end
 
-        url =
-          "https://generativelanguage.googleapis.com/v1beta/models/embedding-001:embedContent\?key\=#{SiteSetting.ai_gemini_api_key}"
+      attr_reader :api_key, :referer
 
+      def perform!(content)
+        headers = { "Referer" => referer, "Content-Type" => "application/json" }
+        url =
+          "https://generativelanguage.googleapis.com/v1beta/models/embedding-001:embedContent\?key\=#{api_key}"
         body = { content: { parts: [{ text: content }] } }
 
         conn = Faraday.new { |f| f.adapter FinalDestination::FaradayAdapter }