discourse-ai/lib/embeddings/vector_representations/multilingual_e5_large.rb

78 lines
2.0 KiB
Ruby

# frozen_string_literal: true
module DiscourseAi
module Embeddings
module VectorRepresentations
class MultilingualE5Large < Base
class << self
def name
"multilingual-e5-large"
end
def correctly_configured?
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_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)
if 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",
self.class.name,
"query: #{text}",
SiteSetting.ai_embeddings_discourse_service_api_key,
)
else
raise "No inference endpoint configured"
end
end
def id
3
end
def version
1
end
def dimensions
1024
end
def max_sequence_length
512
end
def pg_function
"<=>"
end
def pg_index_type
"vector_cosine_ops"
end
def tokenizer
DiscourseAi::Tokenizer::MultilingualE5LargeTokenizer
end
end
end
end
end