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>

lib/integrations/openai/openai_prompts/reply.txt

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+Please suggest a reply to the following conversation between support agents and customer. Don't expose that you are an AI model, respond "Couldn't generate the reply" in cases where you can't answer. Reply in the user\'s language.

lib/integrations/openai/openai_prompts/summary.txt

@@ -0,0 +1 @@
+Please summarize the key points from the following conversation between support agents and customer as bullet points for the next support agent looking into the conversation. Reply in the user's language.

lib/integrations/openai/processor_service.rb

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+class Integrations::Openai::ProcessorService < Integrations::LlmBaseService
+  AGENT_INSTRUCTION = 'You are a helpful support agent.'.freeze
+  LANGUAGE_INSTRUCTION = 'Ensure that the reply should be in user language.'.freeze
+  def reply_suggestion_message
+    make_api_call(reply_suggestion_body)
+  end
+
+  def summarize_message
+    make_api_call(summarize_body)
+  end
+
+  def rephrase_message
+    make_api_call(build_api_call_body("#{AGENT_INSTRUCTION} Please rephrase the following response. " \
+                                      "#{LANGUAGE_INSTRUCTION}"))
+  end
+
+  def fix_spelling_grammar_message
+    make_api_call(build_api_call_body("#{AGENT_INSTRUCTION} Please fix the spelling and grammar of the following response. " \
+                                      "#{LANGUAGE_INSTRUCTION}"))
+  end
+
+  def shorten_message
+    make_api_call(build_api_call_body("#{AGENT_INSTRUCTION} Please shorten the following response. " \
+                                      "#{LANGUAGE_INSTRUCTION}"))
+  end
+
+  def expand_message
+    make_api_call(build_api_call_body("#{AGENT_INSTRUCTION} Please expand the following response. " \
+                                      "#{LANGUAGE_INSTRUCTION}"))
+  end
+
+  def make_friendly_message
+    make_api_call(build_api_call_body("#{AGENT_INSTRUCTION} Please make the following response more friendly. " \
+                                      "#{LANGUAGE_INSTRUCTION}"))
+  end
+
+  def make_formal_message
+    make_api_call(build_api_call_body("#{AGENT_INSTRUCTION} Please make the following response more formal. " \
+                                      "#{LANGUAGE_INSTRUCTION}"))
+  end
+
+  def simplify_message
+    make_api_call(build_api_call_body("#{AGENT_INSTRUCTION} Please simplify the following response. " \
+                                      "#{LANGUAGE_INSTRUCTION}"))
+  end
+
+  private
+
+  def prompt_from_file(file_name, enterprise: false)
+    path = enterprise ? 'enterprise/lib/enterprise/integrations/openai_prompts' : 'lib/integrations/openai/openai_prompts'
+    Rails.root.join(path, "#{file_name}.txt").read
+  end
+
+  def build_api_call_body(system_content, user_content = event['data']['content'])
+    {
+      model: GPT_MODEL,
+      messages: [
+        { role: 'system', content: system_content },
+        { role: 'user', content: user_content }
+      ]
+    }.to_json
+  end
+
+  def conversation_messages(in_array_format: false)
+    messages = init_messages_body(in_array_format)
+
+    add_messages_until_token_limit(conversation, messages, in_array_format)
+  end
+
+  def add_messages_until_token_limit(conversation, messages, in_array_format, start_from = 0)
+    character_count = start_from
+    conversation.messages.where(message_type: [:incoming, :outgoing]).where(private: false).reorder('id desc').each do |message|
+      character_count, message_added = add_message_if_within_limit(character_count, message, messages, in_array_format)
+      break unless message_added
+    end
+    messages
+  end
+
+  def add_message_if_within_limit(character_count, message, messages, in_array_format)
+    content = message.content_for_llm
+    if valid_message?(content, character_count)
+      add_message_to_list(message, messages, in_array_format, content)
+      character_count += content.length
+      [character_count, true]
+    else
+      [character_count, false]
+    end
+  end
+
+  def valid_message?(content, character_count)
+    content.present? && character_count + content.length <= TOKEN_LIMIT
+  end
+
+  def add_message_to_list(message, messages, in_array_format, content)
+    formatted_message = format_message(message, in_array_format, content)
+    messages.prepend(formatted_message)
+  end
+
+  def init_messages_body(in_array_format)
+    in_array_format ? [] : ''
+  end
+
+  def format_message(message, in_array_format, content)
+    in_array_format ? format_message_in_array(message, content) : format_message_in_string(message, content)
+  end
+
+  def format_message_in_array(message, content)
+    { role: (message.incoming? ? 'user' : 'assistant'), content: content }
+  end
+
+  def format_message_in_string(message, content)
+    sender_type = message.incoming? ? 'Customer' : 'Agent'
+    "#{sender_type} #{message.sender&.name} : #{content}\n"
+  end
+
+  def summarize_body
+    {
+      model: GPT_MODEL,
+      messages: [
+        { role: 'system',
+          content: prompt_from_file('summary', enterprise: false) },
+        { role: 'user', content: conversation_messages }
+      ]
+    }.to_json
+  end
+
+  def reply_suggestion_body
+    {
+      model: GPT_MODEL,
+      messages: [
+        { role: 'system',
+          content: prompt_from_file('reply', enterprise: false) }
+      ].concat(conversation_messages(in_array_format: true))
+    }.to_json
+  end
+end
+
+Integrations::Openai::ProcessorService.prepend_mod_with('Integrations::OpenaiProcessorService')