Files
chatwoot/lib/integrations/openai/processor_service.rb
T
2025-12-12 14:00:40 +05:30

178 lines
6.4 KiB
Ruby

class Integrations::Openai::ProcessorService < Integrations::LlmBaseService
LANGUAGE_INSTRUCTION = 'Ensure that the reply should be in user language.'.freeze
AGENT_INSTRUCTION = <<~HEREDOC.freeze
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>
%s
</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
- #{LANGUAGE_INSTRUCTION}
Output only the rewritten message without any preamble, tags or explanation.
HEREDOC
def reply_suggestion_message
make_api_call(reply_suggestion_body)
end
def summarize_message
make_api_call(summarize_body)
end
def confident_message
tone_instruction = determine_tone_instruction('confident')
make_api_call(build_api_call_body(format(AGENT_INSTRUCTION, tone_instruction)))
end
def fix_spelling_grammar_message
make_api_call(build_api_call_body('Please fix the spelling and grammar of the following response. ' \
"#{LANGUAGE_INSTRUCTION}"))
end
def straightforward_message
tone_instruction = determine_tone_instruction('straightforward')
make_api_call(build_api_call_body(format(AGENT_INSTRUCTION, tone_instruction)))
end
def casual_message
tone_instruction = determine_tone_instruction('casual')
make_api_call(build_api_call_body(format(AGENT_INSTRUCTION, tone_instruction)))
end
def make_friendly_message
tone_instruction = determine_tone_instruction('friendly')
make_api_call(build_api_call_body(format(AGENT_INSTRUCTION, tone_instruction)))
end
def make_formal_message
tone_instruction = determine_tone_instruction('formal')
make_api_call(build_api_call_body(format(AGENT_INSTRUCTION, tone_instruction)))
end
def professional_message
tone_instruction = determine_tone_instruction('professional')
make_api_call(build_api_call_body(format(AGENT_INSTRUCTION, tone_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
def determine_tone_instruction(tone)
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
determine_tone_instruction('friendly')
end
end
end
Integrations::Openai::ProcessorService.prepend_mod_with('Integrations::OpenaiProcessorService')