# 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, asymetric: false) text = "#{asymmetric_query_prefix} #{text}" if asymetric 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 "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 "halfvec_ip_ops" end def tokenizer DiscourseAi::Tokenizer::BgeLargeEnTokenizer end 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 end