0b4028b95d
Currently, it’s unclear whether an FAQ item is generated from a document, derived from a conversation, or added manually. This PR resolves the issue by providing visibility into the source of each FAQ. Users can now see whether an FAQ was generated or manually added and, if applicable, by whom. - Move the document_id to a polymorphic relation (documentable). - Updated the APIs to accommodate the change. - Update the service to add corresponding references. - Updated the specs. <img width="1007" alt="Screenshot 2025-01-15 at 11 27 56 PM" src="https://github.com/user-attachments/assets/7d58f798-19c0-4407-b3e2-748a919d14af" /> --------- Co-authored-by: Sivin Varghese <64252451+iamsivin@users.noreply.github.com>
112 lines
3.0 KiB
Ruby
112 lines
3.0 KiB
Ruby
class Captain::Llm::ConversationFaqService < Captain::Llm::BaseOpenAiService
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DISTANCE_THRESHOLD = 0.3
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def initialize(assistant, conversation, model = DEFAULT_MODEL)
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super()
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@assistant = assistant
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@conversation = conversation
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@content = conversation.to_llm_text
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@model = model
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end
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def generate_and_deduplicate
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new_faqs = generate
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return [] if new_faqs.empty?
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duplicate_faqs, unique_faqs = find_and_separate_duplicates(new_faqs)
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save_new_faqs(unique_faqs)
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log_duplicate_faqs(duplicate_faqs) if Rails.env.development?
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end
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private
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attr_reader :content, :conversation, :assistant
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def find_and_separate_duplicates(faqs)
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duplicate_faqs = []
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unique_faqs = []
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faqs.each do |faq|
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combined_text = "#{faq['question']}: #{faq['answer']}"
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embedding = Captain::Llm::EmbeddingService.new.get_embedding(combined_text)
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similar_faqs = find_similar_faqs(embedding)
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if similar_faqs.any?
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duplicate_faqs << { faq: faq, similar_faqs: similar_faqs }
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else
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unique_faqs << faq
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end
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end
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[duplicate_faqs, unique_faqs]
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end
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def find_similar_faqs(embedding)
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similar_faqs = assistant
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.responses
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.nearest_neighbors(:embedding, embedding, distance: 'cosine')
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Rails.logger.debug(similar_faqs.map { |faq| [faq.question, faq.neighbor_distance] })
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similar_faqs.select { |record| record.neighbor_distance < DISTANCE_THRESHOLD }
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end
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def save_new_faqs(faqs)
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faqs.map do |faq|
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assistant.responses.create!(
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question: faq['question'],
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answer: faq['answer'],
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status: 'pending',
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documentable: conversation
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)
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end
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end
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def log_duplicate_faqs(duplicate_faqs)
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return if duplicate_faqs.empty?
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Rails.logger.info "Found #{duplicate_faqs.length} duplicate FAQs:"
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duplicate_faqs.each do |duplicate|
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Rails.logger.info(
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"Q: #{duplicate[:faq]['question']}\n" \
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"A: #{duplicate[:faq]['answer']}\n\n" \
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"Similar existing FAQs: #{duplicate[:similar_faqs].map { |f| "Q: #{f.question} A: #{f.answer}" }.join(', ')}"
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)
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end
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end
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def generate
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response = @client.chat(parameters: chat_parameters)
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parse_response(response)
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rescue OpenAI::Error => e
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Rails.logger.error "OpenAI API Error: #{e.message}"
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[]
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end
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def chat_parameters
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prompt = Captain::Llm::SystemPromptsService.conversation_faq_generator
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{
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model: @model,
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response_format: { type: 'json_object' },
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messages: [
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{
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role: 'system',
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content: prompt
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},
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{
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role: 'user',
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content: content
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}
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]
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}
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end
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def parse_response(response)
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content = response.dig('choices', 0, 'message', 'content')
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return [] if content.nil?
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JSON.parse(content.strip).fetch('faqs', [])
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rescue JSON::ParserError => e
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Rails.logger.error "Error in parsing GPT processed response: #{e.message}"
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[]
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end
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end
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