95 lines
2.6 KiB
Ruby
95 lines
2.6 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 BgeLargeEn < Base
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class << self
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def name
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"bge-large-en"
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end
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def correctly_configured?
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SiteSetting.ai_cloudflare_workers_api_token.present? ||
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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_cloudflare_workers_api_token
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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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text = "#{asymmetric_query_prefix} #{text}" if asymetric
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if SiteSetting.ai_cloudflare_workers_api_token.present?
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DiscourseAi::Inference::CloudflareWorkersAi
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.perform!(inference_model_name, { text: text })
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.dig(:result, :data)
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.first
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elsif 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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inference_model_name.split("/").last,
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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 inference_model_name
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"baai/bge-large-en-v1.5"
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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 id
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4
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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 pg_function
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"<#>"
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end
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def pg_index_type
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"halfvec_ip_ops"
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end
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def tokenizer
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DiscourseAi::Tokenizer::BgeLargeEnTokenizer
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end
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def asymmetric_query_prefix
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"Represent this sentence for searching relevant passages:"
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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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