class NeuroGuideAssistant(EmoVoiceAssistant):
    def __init__(self, config):
        super().__init__(config)
        self.context_stack = []
        self.knowledge_graph = KnowledgeGraphLoader.load(config['knowledge_base'])
        self.adaptive_learning = AdaptiveLearningEngine()

    async def process_command(self, command: str):
        context = self._analyze_context(command)
        intent = await self._detect_intent(command, context)

        if intent == 'navigation':
            await self._handle_navigation(command)
        elif intent == 'context_help':
            await self._provide_context_help()
        else:
            await super().process_command(command)

    async def _handle_navigation(self, command):
        section = self._extract_section(command)
        self.context_stack.append(section)
        content = await self._fetch_content(section)
        self.speak(f"Переход к {section}: {content['description']}")
        await self._suggest_related(section)

    async def _provide_context_help(self):
        current_context = self.context_stack[-1] if self.context_stack else 'root'
        suggestions = self.knowledge_graph.get_suggestions(current_context)
        self.speak(f"Сейчас вы в разделе {current_context}. Возможно вам нужно:")
        for idx, suggestion in enumerate(suggestions, 1):
            self.speak(f"{idx}. {suggestion['description']}")
            await asyncio.sleep(0.5)

    def _analyze_context(self, command):
        return {
            "location": self.context_stack[-1] if self.context_stack else None,
            "user_skill_level": self.adaptive_learning.user_profile['expertise'],
            "system_status": SystemStatusMonitor.get_current_state()
        }

class ContentNavigator:
    def __init__(self, knowledge_base):
        self.graph = KnowledgeGraph(knowledge_base)
        self.history = NavigationHistory()

    async def navigate_to(self, section: str):
        node = self.graph.get_node(section)
        self.history.record(section)
        return {
            "content": node.content,
            "related": self.graph.get_related(section),
            "actions": node.available_actions
        }

class AdaptiveLearningEngine:
    def __init__(self):
        self.user_profile = {
            'expertise': 'beginner',
            'common_errors': [],
            'preferred_actions': []
        }

    def update_profile(self, interaction_data):
        # Анализ паттернов поведения с помощью LSTM
        self.user_profile = self._analyze_interactions(interaction_data)

class QuantumContentMapper:
    def __init__(self, quantum_backend):
        self.backend = quantum_backend

    async def optimize_knowledge_graph(self):
        # Квантовая оптимизация структуры контента
        optimized_graph = await self.backend.optimize_graph(
            self.knowledge_graph,
            optimization_target='user_engagement'
        )
        return QuantumContentMapper.validate_graph(optimized_graph)

# Обновленная конфигурация
content_navigation:
  knowledge_base: 
    - path: "content_graph.json"
  adaptive_learning:
    update_interval: 3600
  quantum_optimization:
    enabled: true
    backend: ibm_quantum
  voice_hints:
    detail_level: adaptive
    suggestion_mode: proactive

voice_ui:
  navigation_commands:
    - "перейти к"
    - "показать"
    - "вернуться"
    - "что дальше"
  help_triggers:
    - "помощь"
    - "что делать"
    - "подскажи варианты"

async def quantum_context_search(self, query):
    # Квантовый поиск по мультиверсионному индексу
    results = await self.quantum_backend.search(
        query=query,
        context=self.context_stack,
        user_profile=self.user_profile
    )
    return self._decode_superposition(results)

def generate_hints(self):
    neural_pattern = self._recognize_behavior_pattern()
    quantum_boosted = self.quantum_optimizer.apply(neural_pattern)
    return self._convert_to_natural_language(quantum_boosted)

class HolographicNavigator:
    def display_3d_graph(self):
        # Проекция голографического интерфейса
        self.projector.render_quantum_graph(
            self.knowledge_graph,
            mode='interactive'
        )

class MemristiveContextMemory:
    def save_context(self):
        # Аналоговое сохранение состояния в мемристорах
        self.memristor_grid.write_pattern(
            self.context_stack,
            weight_matrix=self.attention_weights
        )