Initial commit: Add logistics and order_detail message types

- Add Logistics component with progress tracking
- Add OrderDetail component for order information
- Support data-driven steps and actions
- Add blue color scale to widget SCSS
- Fix node overflow and progress bar rendering issues
- Add English translations for dashboard components

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

enterprise/app/services/captain/llm/conversation_faq_service.rb

@@ -0,0 +1,126 @@
+class Captain::Llm::ConversationFaqService < Llm::BaseAiService
+  include Integrations::LlmInstrumentation
+  DISTANCE_THRESHOLD = 0.3
+
+  def initialize(assistant, conversation)
+    super()
+    @assistant = assistant
+    @conversation = conversation
+    @content = conversation.to_llm_text
+  end
+
+  # Generates and deduplicates FAQs from conversation content
+  # Skips processing if there was no human interaction
+  def generate_and_deduplicate
+    return [] if no_human_interaction?
+
+    new_faqs = generate
+    return [] if new_faqs.empty?
+
+    duplicate_faqs, unique_faqs = find_and_separate_duplicates(new_faqs)
+    save_new_faqs(unique_faqs)
+    log_duplicate_faqs(duplicate_faqs) if Rails.env.development?
+  end
+
+  private
+
+  attr_reader :content, :conversation, :assistant
+
+  def no_human_interaction?
+    conversation.first_reply_created_at.nil?
+  end
+
+  def find_and_separate_duplicates(faqs)
+    duplicate_faqs = []
+    unique_faqs = []
+
+    faqs.each do |faq|
+      combined_text = "#{faq['question']}: #{faq['answer']}"
+      embedding = Captain::Llm::EmbeddingService.new(account_id: @conversation.account_id).get_embedding(combined_text)
+      similar_faqs = find_similar_faqs(embedding)
+
+      if similar_faqs.any?
+        duplicate_faqs << { faq: faq, similar_faqs: similar_faqs }
+      else
+        unique_faqs << faq
+      end
+    end
+
+    [duplicate_faqs, unique_faqs]
+  end
+
+  def find_similar_faqs(embedding)
+    similar_faqs = assistant
+                   .responses
+                   .nearest_neighbors(:embedding, embedding, distance: 'cosine')
+    Rails.logger.debug(similar_faqs.map { |faq| [faq.question, faq.neighbor_distance] })
+    similar_faqs.select { |record| record.neighbor_distance < DISTANCE_THRESHOLD }
+  end
+
+  def save_new_faqs(faqs)
+    faqs.map do |faq|
+      assistant.responses.create!(
+        question: faq['question'],
+        answer: faq['answer'],
+        status: 'pending',
+        documentable: conversation
+      )
+    end
+  end
+
+  def log_duplicate_faqs(duplicate_faqs)
+    return if duplicate_faqs.empty?
+
+    Rails.logger.info "Found #{duplicate_faqs.length} duplicate FAQs:"
+    duplicate_faqs.each do |duplicate|
+      Rails.logger.info(
+        "Q: #{duplicate[:faq]['question']}\n" \
+        "A: #{duplicate[:faq]['answer']}\n\n" \
+        "Similar existing FAQs: #{duplicate[:similar_faqs].map { |f| "Q: #{f.question} A: #{f.answer}" }.join(', ')}"
+      )
+    end
+  end
+
+  def generate
+    response = instrument_llm_call(instrumentation_params) do
+      chat
+        .with_params(response_format: { type: 'json_object' })
+        .with_instructions(system_prompt)
+        .ask(@content)
+    end
+    parse_response(response.content)
+  rescue RubyLLM::Error => e
+    Rails.logger.error "LLM API Error: #{e.message}"
+    []
+  end
+
+  def instrumentation_params
+    {
+      span_name: 'llm.captain.conversation_faq',
+      model: @model,
+      temperature: @temperature,
+      account_id: @conversation.account_id,
+      conversation_id: @conversation.display_id,
+      feature_name: 'conversation_faq',
+      messages: [
+        { role: 'system', content: system_prompt },
+        { role: 'user', content: @content }
+      ],
+      metadata: { assistant_id: @assistant.id }
+    }
+  end
+
+  def system_prompt
+    account_language = @conversation.account.locale_english_name
+    Captain::Llm::SystemPromptsService.conversation_faq_generator(account_language)
+  end
+
+  def parse_response(response)
+    return [] if response.nil?
+
+    JSON.parse(response.strip).fetch('faqs', [])
+  rescue JSON::ParserError => e
+    Rails.logger.error "Error in parsing GPT processed response: #{e.message}"
+    []
+  end
+end