02c467b6db
Adds additional endpoints for OpenAI integration which will allow - Reply Suggestions - Summarization ref: #6436 fixes: https://linear.app/chatwoot/issue/CW-1596/backend-for-generating-conversation-summary
108 lines
3.1 KiB
Ruby
108 lines
3.1 KiB
Ruby
class Integrations::Openai::ProcessorService
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# 3.5 support 4,096 tokens
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# 1 token is approx 4 characters
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# 4,096 * 4 = 16,384 characters, sticking to 15,000 to be safe
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TOKEN_LIMIT = 15_000
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API_URL = 'https://api.openai.com/v1/chat/completions'.freeze
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GPT_MODEL = 'gpt-3.5-turbo'.freeze
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ALLOWED_EVENT_NAMES = %w[rephrase summarize reply_suggestion].freeze
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pattr_initialize [:hook!, :event!]
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def perform
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event_name = event['name']
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return nil unless valid_event_name?(event_name)
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send("#{event_name}_message")
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end
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private
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def valid_event_name?(event_name)
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ALLOWED_EVENT_NAMES.include?(event_name)
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end
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def rephrase_body
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{
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model: GPT_MODEL,
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messages: [
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{ role: 'system',
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content: "You are a helpful support agent. Please rephrase the following response to a more #{event['data']['tone']} tone." },
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{ role: 'user', content: event['data']['content'] }
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]
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}.to_json
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end
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def conversation_messages(in_array_format: false)
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conversation = hook.account.conversations.find_by(display_id: event['data']['conversation_display_id'])
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messages = in_array_format ? [] : ''
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character_count = 0
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conversation.messages.chat.reorder('id desc').each do |message|
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character_count += message.content.length
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break if character_count > TOKEN_LIMIT
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formatted_message = format_message(message, in_array_format)
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messages.prepend(formatted_message)
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end
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messages
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end
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def format_message(message, in_array_format)
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in_array_format ? format_message_in_array(message) : format_message_in_string(message)
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end
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def format_message_in_array(message)
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{ role: (message.incoming? ? 'user' : 'assistant'), content: message.content }
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end
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def format_message_in_string(message)
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sender_type = message.incoming? ? 'Customer' : 'Agent'
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"#{sender_type} #{message.sender&.name} : #{message.content}\n"
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end
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def summarize_body
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{
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model: GPT_MODEL,
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messages: [
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{ role: 'system',
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content: 'Please summarize the key points from the following conversation between support agents and ' \
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'customer as bullet points for the next support agent looking into the conversation' },
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{ role: 'user', content: conversation_messages }
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]
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}.to_json
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end
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def reply_suggestion_body
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{
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model: GPT_MODEL,
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messages: [
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{ role: 'system', content: 'Please suggest a reply to the following conversation between support agents and customer' }
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].concat(conversation_messages(in_array_format: true))
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}.to_json
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end
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def reply_suggestion_message
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make_api_call(reply_suggestion_body)
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end
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def summarize_message
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make_api_call(summarize_body)
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end
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def rephrase_message
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make_api_call(rephrase_body)
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end
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def make_api_call(body)
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headers = {
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'Content-Type' => 'application/json',
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'Authorization' => "Bearer #{hook.settings['api_key']}"
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}
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response = HTTParty.post(API_URL, headers: headers, body: body)
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JSON.parse(response.body)['choices'].first['message']['content']
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end
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end
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