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

95 lines
2.6 KiB
Ruby

# 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
if SiteSetting.ai_cloudflare_workers_api_token.present?
DiscourseAi::Inference::CloudflareWorkersAi
.perform!(inference_model_name, { text: text })
.dig(:result, :data)
.first
elsif 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",
inference_model_name.split("/").last,
text,
SiteSetting.ai_embeddings_discourse_service_api_key,
)
else
raise "No inference endpoint configured"
end
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
"vector_ip_ops"
end
def tokenizer
DiscourseAi::Tokenizer::BgeLargeEnTokenizer
end
def asymmetric_query_prefix
"Represent this sentence for searching relevant passages:"
end
end
end
end
end