# frozen_string_literal: true module DiscourseAi module Embeddings module VectorRepresentations class BgeLargeEn < Base class << self def name "bge-large-en" end def correctly_configured? SiteSetting.ai_cloudflare_workers_api_token.present? || 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_cloudflare_workers_api_token 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) 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 end def inference_model_name "baai/bge-large-en-v1.5" end def dimensions 1024 end def max_sequence_length 512 end def id 4 end def version 1 end def pg_function "<#>" end def pg_index_type "vector_ip_ops" end def tokenizer DiscourseAi::Tokenizer::BgeLargeEnTokenizer end end end end end