conversational-ai-flow

Conversational AI Flow Expert Эксперт по проектированию и реализации потоков разговорного ИИ. Основные принципы дизайна Управление состоянием class ConversationState : def init ( self ) : self . current_intent = None self . entities = { } self . conversation_history = [ ] self . flow_position = "start" self . confidence_threshold = 0.7 def update_context ( self , user_input , intent , entities ) : self . conversation_history . append ( { "user_input" : user_input , "intent" : intent , "entities" : entities } ) self . entities . update ( entities ) self . current_intent = intent Паттерны архитектуры потоков Маршрутизация на основе намерений flows : booking_flow : entry_conditions : - intent : "book_appointment" steps : - name : "collect_datetime" prompt : "When would you like to schedule?" validation : "datetime_validator" - name : "confirm_booking" prompt : "Confirm booking on {datetime}?" actions : [ "create_booking" , "send_confirmation" ] fallback_flow : triggers : [ "low_confidence" , "unknown_intent" ] strategy : "clarification_questions" Паттерн заполнения слотов def slot_filling_handler ( state , required_slots ) : missing_slots = [ s for s in required_slots if s not in state . entities ] if missing_slots : return generate_slot_prompt ( missing_slots [ 0 ] , state ) return proceed_to_next_step ( state ) Обработка ошибок и восстановление Прогрессивное раскрытие class ErrorRecovery : def handle_misunderstanding ( self , state , attempt_count ) : strategies = { 1 : "I didn't quite catch that. Could you rephrase?" , 2 : "Let me try differently. Are you looking to: [options]?" , 3 : "Let me connect you with a human agent." } return strategies . get ( attempt_count , strategies [ 3 ] ) Генерация ответов Контекстуальные шаблоны class ResponseGenerator : templates = { "confirmation" : [ "Got it! {summary}. Is that correct?" , "Let me confirm: {summary}. Does this look right?" ] , "progress" : [ "Great! We've got {completed}. Next, {next_step}." , "Perfect! Just need {remaining} and we're done." ] } Мультимодальные ответы { "response_type" : "rich" , "text" : "Here are your options:" , "components" : [ { "type" : "quick_replies" , "options" : [ { "title" : "Schedule Appointment" , "payload" : "intent:book" } , { "title" : "Check Status" , "payload" : "intent:status" } ] } ] } Аналитика и оптимизация def track_flow_metrics ( conversation_id , metrics ) : return { "completion_rate" : metrics . completed / metrics . started , "average_turns" : metrics . total_turns / metrics . conversations , "fallback_rate" : metrics . fallbacks / metrics . total_turns , "abandonment_points" : identify_drop_off_points ( conversation_id ) } Лучшие практики Определите четкую личность и тон бота Предвосхищайте потребности пользователей Используйте резюме для длинных диалогов Тестируйте все пути и edge cases Мониторьте реальные разговоры для улучшения