Merge branch 'develop' into feature/service-worker-caching

lib/captain/base_task_service.rb

@@ -0,0 +1,181 @@
+class Captain::BaseTaskService
+  include Integrations::LlmInstrumentation
+
+  # gpt-4o-mini supports 128,000 tokens
+  # 1 token is approx 4 characters
+  # sticking with 120000 to be safe
+  # 120000 * 4 = 480,000 characters (rounding off downwards to 400,000 to be safe)
+  TOKEN_LIMIT = 400_000
+  GPT_MODEL = Llm::Config::DEFAULT_MODEL
+
+  # Prepend enterprise module to subclasses when they're defined.
+  # This ensures the enterprise perform wrapper is applied even when
+  # subclasses define their own perform method, since prepend puts
+  # the module before the class in the ancestor chain.
+  def self.inherited(subclass)
+    super
+    subclass.prepend_mod_with('Captain::BaseTaskService')
+  end
+
+  pattr_initialize [:account!, { conversation_display_id: nil }]
+
+  private
+
+  def event_name
+    raise NotImplementedError, "#{self.class} must implement #event_name"
+  end
+
+  def conversation
+    @conversation ||= account.conversations.find_by(display_id: conversation_display_id)
+  end
+
+  def api_base
+    endpoint = InstallationConfig.find_by(name: 'CAPTAIN_OPEN_AI_ENDPOINT')&.value.presence || 'https://api.openai.com/'
+    endpoint = endpoint.chomp('/')
+    "#{endpoint}/v1"
+  end
+
+  def make_api_call(model:, messages:)
+    # Community edition prerequisite checks
+    # Enterprise module handles these with more specific error messages (cloud vs self-hosted)
+    return { error: I18n.t('captain.disabled'), error_code: 403 } unless captain_tasks_enabled?
+    return { error: I18n.t('captain.api_key_missing'), error_code: 401 } unless api_key_configured?
+
+    instrumentation_params = build_instrumentation_params(model, messages)
+
+    response = instrument_llm_call(instrumentation_params) do
+      execute_ruby_llm_request(model: model, messages: messages)
+    end
+
+    # Build follow-up context for client-side refinement, when applicable
+    if build_follow_up_context? && response[:message].present?
+      response.merge(follow_up_context: build_follow_up_context(messages, response))
+    else
+      response
+    end
+  end
+
+  def execute_ruby_llm_request(model:, messages:)
+    Llm::Config.with_api_key(api_key, api_base: api_base) do |context|
+      chat = context.chat(model: model)
+      system_msg = messages.find { |m| m[:role] == 'system' }
+      chat.with_instructions(system_msg[:content]) if system_msg
+
+      conversation_messages = messages.reject { |m| m[:role] == 'system' }
+      return { error: 'No conversation messages provided', error_code: 400, request_messages: messages } if conversation_messages.empty?
+
+      add_messages_if_needed(chat, conversation_messages)
+      response = chat.ask(conversation_messages.last[:content])
+      build_ruby_llm_response(response, messages)
+    end
+  rescue StandardError => e
+    ChatwootExceptionTracker.new(e, account: account).capture_exception
+    { error: e.message, request_messages: messages }
+  end
+
+  def add_messages_if_needed(chat, conversation_messages)
+    return if conversation_messages.length == 1
+
+    conversation_messages[0...-1].each do |msg|
+      chat.add_message(role: msg[:role].to_sym, content: msg[:content])
+    end
+  end
+
+  def build_ruby_llm_response(response, messages)
+    {
+      message: response.content,
+      usage: {
+        'prompt_tokens' => response.input_tokens,
+        'completion_tokens' => response.output_tokens,
+        'total_tokens' => (response.input_tokens || 0) + (response.output_tokens || 0)
+      },
+      request_messages: messages
+    }
+  end
+
+  def build_instrumentation_params(model, messages)
+    {
+      span_name: "llm.#{event_name}",
+      account_id: account.id,
+      conversation_id: conversation&.display_id,
+      feature_name: event_name,
+      model: model,
+      messages: messages,
+      temperature: nil,
+      metadata: instrumentation_metadata
+    }
+  end
+
+  def instrumentation_metadata
+    {
+      channel_type: conversation&.inbox&.channel_type
+    }.compact
+  end
+
+  def conversation_messages(start_from: 0)
+    messages = []
+    character_count = start_from
+
+    conversation.messages
+                .where(message_type: [:incoming, :outgoing])
+                .where(private: false)
+                .reorder('id desc')
+                .each do |message|
+      content = message.content_for_llm
+      break unless content.present? && character_count + content.length <= TOKEN_LIMIT
+
+      messages.prepend({ role: (message.incoming? ? 'user' : 'assistant'), content: content })
+      character_count += content.length
+    end
+
+    messages
+  end
+
+  def captain_tasks_enabled?
+    account.feature_enabled?('captain_tasks')
+  end
+
+  def api_key_configured?
+    api_key.present?
+  end
+
+  def api_key
+    @api_key ||= openai_hook&.settings&.dig('api_key') || system_api_key
+  end
+
+  def openai_hook
+    @openai_hook ||= account.hooks.find_by(app_id: 'openai', status: 'enabled')
+  end
+
+  def system_api_key
+    @system_api_key ||= InstallationConfig.find_by(name: 'CAPTAIN_OPEN_AI_API_KEY')&.value
+  end
+
+  def prompt_from_file(file_name)
+    Rails.root.join('lib/integrations/openai/openai_prompts', "#{file_name}.liquid").read
+  end
+
+  # Follow-up context for client-side refinement
+  def build_follow_up_context?
+    # FollowUpService should return its own updated context
+    !is_a?(Captain::FollowUpService)
+  end
+
+  def build_follow_up_context(messages, response)
+    {
+      event_name: event_name,
+      original_context: extract_original_context(messages),
+      last_response: response[:message],
+      conversation_history: [],
+      channel_type: conversation&.inbox&.channel_type
+    }
+  end
+
+  def extract_original_context(messages)
+    # Get the most recent user message for follow-up context
+    user_msg = messages.reverse.find { |m| m[:role] == 'user' }
+    user_msg ? user_msg[:content] : nil
+  end
+end
+
+Captain::BaseTaskService.prepend_mod_with('Captain::BaseTaskService')

lib/captain/follow_up_service.rb

@@ -0,0 +1,106 @@
+class Captain::FollowUpService < Captain::BaseTaskService
+  pattr_initialize [:account!, :follow_up_context!, :user_message!, { conversation_display_id: nil }]
+
+  ALLOWED_EVENT_NAMES = %w[
+    professional
+    casual
+    friendly
+    confident
+    straightforward
+    fix_spelling_grammar
+    improve
+    summarize
+    reply_suggestion
+    label_suggestion
+  ].freeze
+
+  def perform
+    return { error: 'Follow-up context missing', error_code: 400 } unless valid_follow_up_context?
+
+    # Build context-aware system prompt
+    system_prompt = build_follow_up_system_prompt(follow_up_context)
+
+    # Build full message array (convert history from string keys to symbol keys)
+    history = follow_up_context['conversation_history'].to_a.map do |msg|
+      { role: msg['role'], content: msg['content'] }
+    end
+
+    messages = [
+      { role: 'system', content: system_prompt },
+      { role: 'user', content: follow_up_context['original_context'] },
+      { role: 'assistant', content: follow_up_context['last_response'] },
+      *history,
+      { role: 'user', content: user_message }
+    ]
+
+    response = make_api_call(model: GPT_MODEL, messages: messages)
+    return response if response[:error]
+
+    response.merge(follow_up_context: update_follow_up_context(user_message, response[:message]))
+  end
+
+  private
+
+  def build_follow_up_system_prompt(session_data)
+    action_context = describe_previous_action(session_data['event_name'])
+
+    <<~PROMPT
+      You just performed a #{action_context} action for a customer support agent.
+      Your job now is to help them refine the result based on their feedback.
+      Be concise and focused on their specific request.
+      Output only the reply, no preamble, tags, or explanation.
+    PROMPT
+  end
+
+  def describe_previous_action(event_name)
+    case event_name
+    when 'professional', 'casual', 'friendly', 'confident', 'straightforward'
+      "tone rewrite (#{event_name})"
+    when 'fix_spelling_grammar'
+      'spelling and grammar correction'
+    when 'improve'
+      'message improvement'
+    when 'summarize'
+      'conversation summary'
+    when 'reply_suggestion'
+      'reply suggestion'
+    when 'label_suggestion'
+      'label suggestion'
+    else
+      event_name
+    end
+  end
+
+  def valid_follow_up_context?
+    return false unless follow_up_context.is_a?(Hash)
+    return false unless ALLOWED_EVENT_NAMES.include?(follow_up_context['event_name'])
+
+    required_keys = %w[event_name original_context last_response]
+    required_keys.all? { |key| follow_up_context[key].present? }
+  end
+
+  def update_follow_up_context(user_msg, assistant_msg)
+    updated_history = follow_up_context['conversation_history'].to_a + [
+      { 'role' => 'user', 'content' => user_msg },
+      { 'role' => 'assistant', 'content' => assistant_msg }
+    ]
+
+    {
+      'event_name' => follow_up_context['event_name'],
+      'original_context' => follow_up_context['original_context'],
+      'last_response' => assistant_msg,
+      'conversation_history' => updated_history,
+      'channel_type' => follow_up_context['channel_type']
+    }
+  end
+
+  def instrumentation_metadata
+    {
+      channel_type: conversation&.inbox&.channel_type || follow_up_context['channel_type']
+    }.compact
+  end
+
+  def event_name
+    'follow_up'
+  end
+end

lib/captain/label_suggestion_service.rb

@@ -0,0 +1,93 @@
+class Captain::LabelSuggestionService < Captain::BaseTaskService
+  pattr_initialize [:account!, :conversation_display_id!]
+
+  def perform
+    # Check cache first
+    cached_response = read_from_cache
+    return cached_response if cached_response.present?
+
+    # Build content
+    content = labels_with_messages
+    return nil if content.blank?
+
+    # Make API call
+    response = make_api_call(
+      model: GPT_MODEL, # TODO: Use separate model for label suggestion
+      messages: [
+        { role: 'system', content: prompt_from_file('label_suggestion') },
+        { role: 'user', content: content }
+      ]
+    )
+    return response if response[:error].present?
+
+    # Clean up response
+    result = { message: response[:message] ? response[:message].gsub(/^(label|labels):/i, '') : '' }
+
+    # Cache successful result
+    write_to_cache(result)
+
+    result
+  end
+
+  private
+
+  def cache_key
+    return nil unless conversation
+
+    format(
+      ::Redis::Alfred::OPENAI_CONVERSATION_KEY,
+      event_name: 'label_suggestion',
+      conversation_id: conversation.id,
+      updated_at: conversation.last_activity_at.to_i
+    )
+  end
+
+  def read_from_cache
+    return nil unless cache_key
+
+    cached = Redis::Alfred.get(cache_key)
+    JSON.parse(cached, symbolize_names: true) if cached.present?
+  rescue JSON::ParserError
+    nil
+  end
+
+  def write_to_cache(response)
+    Redis::Alfred.setex(cache_key, response.to_json) if cache_key
+  end
+
+  def labels_with_messages
+    return nil unless valid_conversation?(conversation)
+
+    labels = account.labels.pluck(:title).join(', ')
+    messages = format_messages_as_string(start_from: labels.length)
+
+    return nil if messages.blank? || labels.blank?
+
+    "Messages:\n#{messages}\nLabels:\n#{labels}"
+  end
+
+  def format_messages_as_string(start_from: 0)
+    messages = conversation_messages(start_from: start_from)
+    messages.map do |msg|
+      sender_type = msg[:role] == 'user' ? 'Customer' : 'Agent'
+      "#{sender_type}: #{msg[:content]}\n"
+    end.join
+  end
+
+  def valid_conversation?(conversation)
+    return false if conversation.nil?
+    return false if conversation.messages.incoming.count < 3
+    return false if conversation.messages.count > 100
+    return false if conversation.messages.count > 20 && !conversation.messages.last.incoming?
+
+    true
+  end
+
+  def event_name
+    'label_suggestion'
+  end
+
+  def build_follow_up_context?
+    false
+  end
+end

lib/captain/reply_suggestion_service.rb

@@ -0,0 +1,40 @@
+class Captain::ReplySuggestionService < Captain::BaseTaskService
+  pattr_initialize [:account!, :conversation_display_id!, :user!]
+
+  def perform
+    make_api_call(
+      model: GPT_MODEL,
+      messages: [
+        { role: 'system', content: system_prompt },
+        { role: 'user', content: formatted_conversation }
+      ]
+    )
+  end
+
+  private
+
+  def system_prompt
+    template = prompt_from_file('reply')
+    render_liquid_template(template, prompt_variables)
+  end
+
+  def prompt_variables
+    {
+      'channel_type' => conversation.inbox.channel_type,
+      'agent_name' => user.name,
+      'agent_signature' => user.message_signature.presence
+    }
+  end
+
+  def render_liquid_template(template_content, variables = {})
+    Liquid::Template.parse(template_content).render(variables)
+  end
+
+  def formatted_conversation
+    LlmFormatter::ConversationLlmFormatter.new(conversation).format(token_limit: TOKEN_LIMIT)
+  end
+
+  def event_name
+    'reply_suggestion'
+  end
+end

lib/captain/rewrite_service.rb

@@ -0,0 +1,59 @@
+class Captain::RewriteService < Captain::BaseTaskService
+  pattr_initialize [:account!, :content!, :operation!, { conversation_display_id: nil }]
+
+  TONE_OPERATIONS = %i[casual professional friendly confident straightforward].freeze
+  ALLOWED_OPERATIONS = (%i[fix_spelling_grammar improve] + TONE_OPERATIONS).freeze
+
+  def perform
+    operation_sym = operation.to_sym
+    raise ArgumentError, "Invalid operation: #{operation}" unless ALLOWED_OPERATIONS.include?(operation_sym)
+
+    send(operation_sym)
+  end
+
+  TONE_OPERATIONS.each do |tone|
+    define_method(tone) do
+      call_llm_with_prompt(tone_rewrite_prompt(tone.to_s))
+    end
+  end
+
+  private
+
+  def fix_spelling_grammar
+    call_llm_with_prompt(prompt_from_file('fix_spelling_grammar'))
+  end
+
+  def improve
+    template = prompt_from_file('improve')
+
+    system_prompt = render_liquid_template(template, {
+                                             'conversation_context' => conversation.to_llm_text(include_contact_details: true),
+                                             'draft_message' => content
+                                           })
+
+    call_llm_with_prompt(system_prompt, content)
+  end
+
+  def call_llm_with_prompt(system_content, user_content = content)
+    make_api_call(
+      model: GPT_MODEL,
+      messages: [
+        { role: 'system', content: system_content },
+        { role: 'user', content: user_content }
+      ]
+    )
+  end
+
+  def render_liquid_template(template_content, variables = {})
+    Liquid::Template.parse(template_content).render(variables)
+  end
+
+  def tone_rewrite_prompt(tone)
+    template = prompt_from_file('tone_rewrite')
+    render_liquid_template(template, 'tone' => tone)
+  end
+
+  def event_name
+    operation
+  end
+end

lib/captain/summary_service.rb

@@ -0,0 +1,19 @@
+class Captain::SummaryService < Captain::BaseTaskService
+  pattr_initialize [:account!, :conversation_display_id!]
+
+  def perform
+    make_api_call(
+      model: GPT_MODEL,
+      messages: [
+        { role: 'system', content: prompt_from_file('summary') },
+        { role: 'user', content: conversation.to_llm_text(include_contact_details: false) }
+      ]
+    )
+  end
+
+  private
+
+  def event_name
+    'summarize'
+  end
+end

lib/filters/filter_keys.yml

@@ -86,12 +86,6 @@ conversations:
     filter_operators:
       - "equal_to"
       - "not_equal_to"
-  country_code:
-    attribute_type: "additional_attributes"
-    data_type: "text"
-    filter_operators:
-      - "equal_to"
-      - "not_equal_to"
   referer:
     attribute_type: "additional_attributes"
     data_type: "link"

lib/integrations/llm_base_service.rb

@@ -7,7 +7,8 @@ class Integrations::LlmBaseService
   # 120000 * 4 = 480,000 characters (rounding off downwards to 400,000 to be safe)
   TOKEN_LIMIT = 400_000
   GPT_MODEL = Llm::Config::DEFAULT_MODEL
-  ALLOWED_EVENT_NAMES = %w[rephrase summarize reply_suggestion fix_spelling_grammar shorten expand make_friendly make_formal simplify].freeze
+  ALLOWED_EVENT_NAMES = %w[summarize reply_suggestion fix_spelling_grammar casual professional friendly confident
+                           straightforward improve].freeze
   CACHEABLE_EVENTS = %w[].freeze
 
   pattr_initialize [:hook!, :event!]

lib/integrations/llm_instrumentation.rb

@@ -30,7 +30,6 @@ module Integrations::LlmInstrumentation
     result = nil
     executed = false
     tracer.in_span(params[:span_name]) do |span|
-      set_request_attributes(span, params)
       set_metadata_attributes(span, params)
 
       # By default, the input and output of a trace are set from the root observation

lib/integrations/openai/openai_prompts/fix_spelling_grammar.liquid

@@ -0,0 +1,18 @@
+You are an AI writing assistant integrated into Chatwoot, an omnichannel customer support platform. Your task is to fix grammar and spelling in a customer support message while preserving the original meaning, intent, and tone.
+
+You will receive a message and must return a corrected version with only grammar, spelling, and punctuation fixes applied.
+
+Important guidelines:
+- Preserve the original meaning, intent, and tone exactly
+- Do not rephrase, rewrite, or change wording beyond grammar, spelling, and punctuation
+- Do not add or remove any information
+- Do not simplify, shorten, or expand the message
+- Ensure the output remains appropriate for customer support
+
+Super Important:
+- If the message has some markdown formatting, keep the formatting as it is.
+- Block quotes (lines starting with >) contain quoted text from the customer's previous message. Preserve this quoted text exactly as written (do not modify the customer's words inside the block quote), but DO improve the agent's reply that follows the block quote.
+- Ensure the output is in the user's original language
+- If the message contains a signature block (text after a `--` line), preserve the signature exactly as written without any modification. Do not add a signature if one is not already present.
+
+Output only the corrected message, with no preamble, tags, or explanation.

lib/integrations/openai/openai_prompts/improve.liquid

@@ -0,0 +1,44 @@
+You are a writing assistant for customer support agents. Your task is to improve a draft message by enhancing its language, clarity, and tone—not by adding new content.
+
+<conversation_context>
+{{ conversation_context }}
+</conversation_context>
+
+<draft_message>
+{{ draft_message }}
+</draft_message>
+
+## Your Task
+
+Rewrite the draft to be clearer, warmer, and more professional while preserving the agent's intent.
+
+## What "Improve" Means
+
+Improve the **quality** of the message, not the **quantity** of information:
+
+| DO | DON'T |
+|-----|--------|
+| Fix grammar, spelling, punctuation | Add new information or steps |
+| Improve sentence structure and flow | Expand scope beyond the draft |
+| Make tone warmer and more professional | Add offers ("I can also...", "Would you like...") |
+| Use contact's name naturally | Invent technical details, links, or examples |
+| Make vague phrases more natural | Turn a brief answer into a long one |
+
+## Using the Context
+
+Use the conversation context to:
+- Understand what's being discussed (so improvements make sense)
+- Gauge appropriate tone (formal/casual, frustrated customer, etc.)
+- Personalize with the contact's name when natural
+
+Do NOT use the context to fill in gaps or add information the agent didn't include.
+
+## Output Rules
+
+- Keep the improved message at a similar length to the draft (brief stays brief)
+- Preserve any markdown formatting
+- Block quotes (lines starting with `>`) contain quoted customer text—keep this unchanged, only improve the agent's reply
+- If the message contains a signature block (text after a `--` line), preserve the signature exactly as written without any modification. Do not add a signature if one is not already present.
+- Output in the same language as the draft
+- Output only the improved message, no commentary
+

lib/integrations/openai/openai_prompts/label_suggestion.liquid

@@ -0,0 +1 @@
+Your role is as an assistant to a customer support agent. You will be provided with a transcript of a conversation between a customer and the support agent, along with a list of potential labels. Your task is to analyze the conversation and select the two labels from the given list that most accurately represent the themes or issues discussed.  Ensure you preserve the exact casing of the labels as they are provided in the list. Do not create new labels; only choose from those provided. Once you have made your selections, please provide your response as a comma-separated list of the provided labels. Remember, your response should only contain the labels you've selected,in their original casing, and nothing else.

lib/integrations/openai/openai_prompts/reply.liquid

@@ -0,0 +1,35 @@
+You are helping a customer support agent draft their next reply. The agent will send this message directly to the customer.
+
+You will receive a conversation with messages labeled by sender:
+- "User:" = customer messages
+- "Support Agent:" = human agent messages
+- "Bot:" = automated bot messages
+
+{% if channel_type == 'Channel::Email' %}
+This is an EMAIL conversation. Write a professional email reply that:
+- Uses appropriate email formatting (greeting, body, sign-off)
+- Is detailed and thorough where needed
+- Maintains a professional tone
+{% if agent_signature %}
+- End with the agent's signature exactly as provided below:
+
+{{ agent_signature }}
+{% else %}
+- End with a professional sign-off using the agent's name: {{ agent_name }}
+{% endif %}
+{% else %}
+This is a CHAT conversation. Write a brief, conversational reply that:
+- Is short and easy to read
+- Gets to the point quickly
+- Does not include formal greetings or sign-offs
+{% endif %}
+
+General guidelines:
+- Address the customer's most recent message directly
+- If a support agent has spoken before, match their writing style
+- If only bot messages exist, write a natural first message
+- Move the conversation forward
+- Do not invent product details, policies, or links that weren't mentioned
+- Reply in the customer's language
+
+Output only the reply.

lib/integrations/openai/openai_prompts/reply.txt

@@ -1 +0,0 @@
-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.liquid

@@ -0,0 +1,28 @@
+As an AI-powered summarization tool, your task is to condense lengthy interactions between customer support agents and customers into brief, digestible summaries. The objective of these summaries is to provide a quick overview, enabling any agent, even those without prior context, to grasp the essence of the conversation promptly.
+
+Make sure you strongly adhere to the following rules when generating the summary
+
+1. Be brief and concise. The shorter the summary the better.
+2. Aim to summarize the conversation in approximately 200 words, formatted as multiple small paragraphs that are easier to read.
+3. Describe the customer intent in around 50 words.
+4. Remove information that is not directly relevant to the customer's problem or the agent's solution. For example, personal anecdotes, small talk, etc.
+5. Don't include segments of the conversation that didn't contribute meaningful content, like greetings or farewell.
+6. The 'Action Items' should be a bullet list, arranged in order of priority if possible.
+7. 'Action Items' should strictly encapsulate tasks committed to by the agent or left incomplete. Any suggestions made by the agent should not be included.
+8. The 'Action Items' should be brief and concise
+9. Mark important words or parts of sentences as bold.
+10. Apply markdown syntax to format any included code, using backticks.
+11. Include a section for "Follow-up Items" or "Open Questions" if there are any unresolved issues or outstanding questions.
+12. If any section does not have any content, remove that section and the heading from the response
+13. Do not insert your own opinions about the conversation.
+
+
+Reply in the user's language, as a markdown of the following format.
+
+**Customer Intent**
+
+**Conversation Summary**
+
+**Action Items**
+
+**Follow-up Items**

lib/integrations/openai/openai_prompts/summary.txt

@@ -1 +0,0 @@
-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/openai_prompts/tone_rewrite.liquid

@@ -0,0 +1,36 @@
+You are an AI writing assistant integrated into Chatwoot, an omnichannel customer support platform. Your task is to rewrite customer support message to match a specific tone while preserving the original meaning and intent.
+
+Here is the tone to apply to the message you will receive:
+<tone_instruction>
+{% case tone %}
+{% when 'friendly' %}
+Warm, approachable, and personable. Use conversational language, positive words, and show empathy. May include phrases like "Happy to help!" or "I'd be glad to..."
+{% when 'confident' %}
+Assertive and assured. Use definitive language, avoid hedging words like "maybe" or "I think". Be direct and authoritative while remaining helpful.
+{% when 'straightforward' %}
+Clear, direct, and to-the-point. Remove unnecessary words, get straight to the information or solution. No fluff or extra pleasantries.
+{% when 'casual' %}
+Relaxed and informal. Use contractions, simpler words, and a conversational style. Friendly but less formal than professional tone.
+{% when 'professional' %}
+Formal, polished, and business-appropriate. Use complete sentences, proper grammar, and maintain respectful distance. Avoid slang or overly casual language.
+{% else %}
+Warm, approachable, and personable. Use conversational language, positive words, and show empathy. May include phrases like "Happy to help!" or "I'd be glad to..."
+{% endcase %}
+</tone_instruction>
+
+Your task is to rewrite the message according to the specified tone instructions.
+
+Important guidelines:
+- Preserve the core meaning and all important information from the original message
+- Keep the rewritten message concise and appropriate for customer support
+- Maintain helpfulness and respect regardless of tone
+- Do not add information that wasn't in the original message
+- Do not remove critical details or instructions
+
+Super Important:
+- If the message has some markdown formatting, keep the formatting as it is.
+- Block quotes (lines starting with >) contain quoted text from the customer's previous message. Preserve this quoted text exactly as written (do not modify the customer's words inside the block quote), but DO improve the agent's reply that follows the block quote.
+- Ensure the output is in the user's original language
+- If the message contains a signature block (text after a `--` line), preserve the signature exactly as written without any modification. Do not add a signature if one is not already present.
+
+Output only the rewritten message without any preamble, tags or explanation.

lib/integrations/openai/processor_service.rb

@@ -1,138 +0,0 @@
-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')

lib/llm/config.rb

@@ -1,7 +1,8 @@
 require 'ruby_llm'
 
 module Llm::Config
-  DEFAULT_MODEL = 'gpt-4o-mini'.freeze
+  DEFAULT_MODEL = 'gpt-4.1-mini'.freeze
+
   class << self
     def initialized?
       @initialized ||= false

lib/llm/models.rb

@@ -0,0 +1,41 @@
+module Llm::Models
+  CONFIG = YAML.load_file(Rails.root.join('config/llm.yml')).freeze
+
+  class << self
+    def providers = CONFIG['providers']
+    def models = CONFIG['models']
+    def features = CONFIG['features']
+    def feature_keys = CONFIG['features'].keys
+
+    def default_model_for(feature)
+      CONFIG.dig('features', feature.to_s, 'default')
+    end
+
+    def models_for(feature)
+      CONFIG.dig('features', feature.to_s, 'models') || []
+    end
+
+    def valid_model_for?(feature, model_name)
+      models_for(feature).include?(model_name.to_s)
+    end
+
+    def feature_config(feature_key)
+      feature = features[feature_key.to_s]
+      return nil unless feature
+
+      {
+        models: feature['models'].map do |model_name|
+          model = models[model_name]
+          {
+            id: model_name,
+            display_name: model['display_name'],
+            provider: model['provider'],
+            coming_soon: model['coming_soon'],
+            credit_multiplier: model['credit_multiplier']
+          }
+        end,
+        default: feature['default']
+      }
+    end
+  end
+end

lib/tasks/download_report.rake

@@ -0,0 +1,183 @@
+# Download Report Rake Tasks
+#
+# Usage:
+#   POSTGRES_STATEMENT_TIMEOUT=600s NEW_RELIC_AGENT_ENABLED=false bundle exec rake download_report:agent
+#   POSTGRES_STATEMENT_TIMEOUT=600s NEW_RELIC_AGENT_ENABLED=false bundle exec rake download_report:inbox
+#   POSTGRES_STATEMENT_TIMEOUT=600s NEW_RELIC_AGENT_ENABLED=false bundle exec rake download_report:label
+#
+# The task will prompt for:
+#   - Account ID
+#   - Start Date (YYYY-MM-DD)
+#   - End Date (YYYY-MM-DD)
+#   - Timezone Offset (e.g., 0, 5.5, -5)
+#   - Business Hours (y/n) - whether to use business hours for time metrics
+#
+# Output: <account_id>_<type>_<start_date>_<end_date>.csv
+
+require 'csv'
+
+# rubocop:disable Metrics/CyclomaticComplexity
+# rubocop:disable Metrics/AbcSize
+# rubocop:disable Metrics/MethodLength
+# rubocop:disable Metrics/ModuleLength
+module DownloadReportTasks
+  def self.prompt(message)
+    print "#{message}: "
+    $stdin.gets.chomp
+  end
+
+  def self.collect_params
+    account_id = prompt('Enter Account ID')
+    abort 'Error: Account ID is required' if account_id.blank?
+
+    account = Account.find_by(id: account_id)
+    abort "Error: Account with ID '#{account_id}' not found" unless account
+
+    start_date = prompt('Enter Start Date (YYYY-MM-DD)')
+    abort 'Error: Start date is required' if start_date.blank?
+
+    end_date = prompt('Enter End Date (YYYY-MM-DD)')
+    abort 'Error: End date is required' if end_date.blank?
+
+    timezone_offset = prompt('Enter Timezone Offset (e.g., 0, 5.5, -5)')
+    timezone_offset = timezone_offset.blank? ? 0 : timezone_offset.to_f
+
+    business_hours = prompt('Use Business Hours? (y/n)')
+    business_hours = business_hours.downcase == 'y'
+
+    begin
+      tz = ActiveSupport::TimeZone[timezone_offset]
+      abort "Error: Invalid timezone offset '#{timezone_offset}'" unless tz
+
+      since = tz.parse("#{start_date} 00:00:00").to_i.to_s
+      until_date = tz.parse("#{end_date} 23:59:59").to_i.to_s
+    rescue StandardError => e
+      abort "Error parsing dates: #{e.message}"
+    end
+
+    {
+      account: account,
+      params: { since: since, until: until_date, timezone_offset: timezone_offset, business_hours: business_hours },
+      start_date: start_date,
+      end_date: end_date
+    }
+  end
+
+  def self.save_csv(filename, headers, rows)
+    CSV.open(filename, 'w') do |csv|
+      csv << headers
+      rows.each { |row| csv << row }
+    end
+    puts "Report saved to: #{filename}"
+  end
+
+  def self.format_time(seconds)
+    return '' if seconds.nil? || seconds.zero?
+
+    seconds.round(2)
+  end
+
+  def self.download_agent_report
+    data = collect_params
+    account = data[:account]
+
+    puts "\nGenerating agent report..."
+    builder = V2::Reports::AgentSummaryBuilder.new(account: account, params: data[:params])
+    report = builder.build
+
+    users = account.users.index_by(&:id)
+    headers = %w[id name email conversations_count resolved_conversations_count avg_resolution_time avg_first_response_time avg_reply_time]
+
+    rows = report.map do |row|
+      user = users[row[:id]]
+      [
+        row[:id],
+        user&.name || 'Unknown',
+        user&.email || 'Unknown',
+        row[:conversations_count],
+        row[:resolved_conversations_count],
+        format_time(row[:avg_resolution_time]),
+        format_time(row[:avg_first_response_time]),
+        format_time(row[:avg_reply_time])
+      ]
+    end
+
+    filename = "#{account.id}_agent_#{data[:start_date]}_#{data[:end_date]}.csv"
+    save_csv(filename, headers, rows)
+  end
+
+  def self.download_inbox_report
+    data = collect_params
+    account = data[:account]
+
+    puts "\nGenerating inbox report..."
+    builder = V2::Reports::InboxSummaryBuilder.new(account: account, params: data[:params])
+    report = builder.build
+
+    inboxes = account.inboxes.index_by(&:id)
+    headers = %w[id name conversations_count resolved_conversations_count avg_resolution_time avg_first_response_time avg_reply_time]
+
+    rows = report.map do |row|
+      inbox = inboxes[row[:id]]
+      [
+        row[:id],
+        inbox&.name || 'Unknown',
+        row[:conversations_count],
+        row[:resolved_conversations_count],
+        format_time(row[:avg_resolution_time]),
+        format_time(row[:avg_first_response_time]),
+        format_time(row[:avg_reply_time])
+      ]
+    end
+
+    filename = "#{account.id}_inbox_#{data[:start_date]}_#{data[:end_date]}.csv"
+    save_csv(filename, headers, rows)
+  end
+
+  def self.download_label_report
+    data = collect_params
+    account = data[:account]
+
+    puts "\nGenerating label report..."
+    builder = V2::Reports::LabelSummaryBuilder.new(account: account, params: data[:params])
+    report = builder.build
+
+    headers = %w[id name conversations_count resolved_conversations_count avg_resolution_time avg_first_response_time avg_reply_time]
+
+    rows = report.map do |row|
+      [
+        row[:id],
+        row[:name],
+        row[:conversations_count],
+        row[:resolved_conversations_count],
+        format_time(row[:avg_resolution_time]),
+        format_time(row[:avg_first_response_time]),
+        format_time(row[:avg_reply_time])
+      ]
+    end
+
+    filename = "#{account.id}_label_#{data[:start_date]}_#{data[:end_date]}.csv"
+    save_csv(filename, headers, rows)
+  end
+end
+# rubocop:enable Metrics/CyclomaticComplexity
+# rubocop:enable Metrics/AbcSize
+# rubocop:enable Metrics/MethodLength
+# rubocop:enable Metrics/ModuleLength
+
+namespace :download_report do
+  desc 'Download agent summary report as CSV'
+  task agent: :environment do
+    DownloadReportTasks.download_agent_report
+  end
+
+  desc 'Download inbox summary report as CSV'
+  task inbox: :environment do
+    DownloadReportTasks.download_inbox_report
+  end
+
+  desc 'Download label summary report as CSV'
+  task label: :environment do
+    DownloadReportTasks.download_label_report
+  end
+end

lib/tasks/ops/cleanup_orphan_conversations.rake

@@ -15,13 +15,13 @@ namespace :chatwoot do
       days_input = $stdin.gets.strip
       days = days_input.empty? ? 7 : days_input.to_i
 
-      # Build a common base relation with identical joins for OR compatibility
+      service = Internal::RemoveOrphanConversationsService.new(account: account, days: days)
+
+      # Preview count using the same query logic
       base = account
              .conversations
              .where('conversations.created_at > ?', days.days.ago)
              .left_outer_joins(:contact, :inbox)
-
-      # Find conversations whose associated contact or inbox record is missing
       conversations = base.where(contacts: { id: nil }).or(base.where(inboxes: { id: nil }))
 
       count = conversations.count
@@ -31,8 +31,8 @@ namespace :chatwoot do
         print 'Do you want to delete these conversations? (y/N): '
         confirm = $stdin.gets.strip.downcase
         if %w[y yes].include?(confirm)
-          conversations.destroy_all
-          puts 'Conversations deleted.'
+          total_deleted = service.perform
+          puts "#{total_deleted} conversations deleted."
         else
           puts 'No conversations were deleted.'
         end