89 lines
2.4 KiB
Ruby
89 lines
2.4 KiB
Ruby
# frozen_string_literal: true
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module DiscourseAi
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module Embeddings
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module VectorRepresentations
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class MultilingualE5Large < Base
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class << self
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def name
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"multilingual-e5-large"
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end
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def correctly_configured?
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DiscourseAi::Inference::HuggingFaceTextEmbeddings.configured? ||
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(
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SiteSetting.ai_embeddings_discourse_service_api_endpoint_srv.present? ||
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SiteSetting.ai_embeddings_discourse_service_api_endpoint.present?
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)
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end
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def dependant_setting_names
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%w[
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ai_hugging_face_tei_endpoint_srv
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ai_hugging_face_tei_endpoint
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ai_embeddings_discourse_service_api_key
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ai_embeddings_discourse_service_api_endpoint_srv
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ai_embeddings_discourse_service_api_endpoint
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]
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end
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end
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def id
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3
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end
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def version
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1
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end
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def dimensions
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1024
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end
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def max_sequence_length
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512
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end
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def pg_function
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"<=>"
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end
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def tokenizer
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DiscourseAi::Tokenizer::MultilingualE5LargeTokenizer
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end
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def inference_client
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if DiscourseAi::Inference::HuggingFaceTextEmbeddings.configured?
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DiscourseAi::Inference::HuggingFaceTextEmbeddings.instance
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elsif SiteSetting.ai_embeddings_discourse_service_api_endpoint_srv.present? ||
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SiteSetting.ai_embeddings_discourse_service_api_endpoint.present?
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DiscourseAi::Inference::DiscourseClassifier.instance(self.class.name)
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else
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raise "No inference endpoint configured"
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end
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end
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def prepare_text(text, asymetric: false)
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prepared_text = super(text, asymetric: asymetric)
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if prepared_text.present? && inference_client.class.name.include?("DiscourseClassifier")
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return "query: #{prepared_text}"
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end
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prepared_text
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end
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def prepare_target_text(target)
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prepared_text = super(target)
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if prepared_text.present? && inference_client.class.name.include?("DiscourseClassifier")
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return "query: #{prepared_text}"
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end
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prepared_text
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end
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end
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end
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end
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end
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