78 lines
2.0 KiB
Ruby
78 lines
2.0 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 vector_from(text, asymetric: false)
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if DiscourseAi::Inference::HuggingFaceTextEmbeddings.configured?
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truncated_text = tokenizer.truncate(text, max_sequence_length - 2)
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DiscourseAi::Inference::HuggingFaceTextEmbeddings.perform!(truncated_text).first
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elsif discourse_embeddings_endpoint.present?
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DiscourseAi::Inference::DiscourseClassifier.perform!(
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"#{discourse_embeddings_endpoint}/api/v1/classify",
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self.class.name,
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"query: #{text}",
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SiteSetting.ai_embeddings_discourse_service_api_key,
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)
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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 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 pg_index_type
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"vector_cosine_ops"
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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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end
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end
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end
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end
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