# frozen_string_literal: true module DiscourseAi module Embeddings module VectorRepresentations class MultilingualE5Large < Base class << self def name "multilingual-e5-large" end def correctly_configured? DiscourseAi::Inference::HuggingFaceTextEmbeddings.configured? || ( SiteSetting.ai_embeddings_discourse_service_api_endpoint_srv.present? || SiteSetting.ai_embeddings_discourse_service_api_endpoint.present? ) end def dependant_setting_names %w[ ai_hugging_face_tei_endpoint_srv ai_hugging_face_tei_endpoint ai_embeddings_discourse_service_api_key ai_embeddings_discourse_service_api_endpoint_srv ai_embeddings_discourse_service_api_endpoint ] end 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, ) else raise "No inference endpoint configured" end end def id 3 end def version 1 end def dimensions 1024 end def max_sequence_length 512 end def pg_function "<=>" end def pg_index_type "vector_cosine_ops" end def tokenizer DiscourseAi::Tokenizer::MultilingualE5LargeTokenizer end end end end end