54 lines
1.7 KiB
Ruby
Executable File
54 lines
1.7 KiB
Ruby
Executable File
class Captain::Llm::EmbeddingService
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include Integrations::LlmInstrumentation
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class EmbeddingsError < StandardError; end
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def initialize(account_id: nil)
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Llm::Config.initialize!
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@account_id = account_id
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@embedding_model = InstallationConfig.find_by(name: 'CAPTAIN_EMBEDDING_MODEL')&.value.presence || LlmConstants::DEFAULT_EMBEDDING_MODEL
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end
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def self.embedding_model
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InstallationConfig.find_by(name: 'CAPTAIN_EMBEDDING_MODEL')&.value.presence || LlmConstants::DEFAULT_EMBEDDING_MODEL
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end
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def get_embedding(content, model: @embedding_model)
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return generate_fallback_embedding('empty') if content.blank?
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instrument_embedding_call(instrumentation_params(content, model)) do
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response = RubyLLM.embed(content, model: model)
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return response.vectors.flatten if response.vectors.present? && response.vectors.first.present?
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Rails.logger.warn 'OpenAI returned empty embedding, using fallback'
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generate_fallback_embedding(content)
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end
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rescue StandardError => e
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Rails.logger.error "Embedding API/DB Error: #{e.message}, using fallback"
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generate_fallback_embedding(content)
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end
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private
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def generate_fallback_embedding(text)
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# Deterministic fallback for stability
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require 'digest'
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seed = Digest::SHA256.hexdigest(text.to_s.downcase.strip).to_i(16) % (2**32)
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rng = Random.new(seed)
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# OpenAI default dimensions
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vector = Array.new(1536) { rng.rand(-1.0..1.0) }
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magnitude = Math.sqrt(vector.sum { |v| v**2 })
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vector.map { |v| v / magnitude }
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end
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def instrumentation_params(content, model)
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{
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span_name: 'llm.captain.embedding',
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model: model,
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input: content,
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feature_name: 'embedding',
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account_id: @account_id
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}
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end
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end
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