tog_03.py

def iterative_reasoning(self, query: str) -> Tuple[List[Dict], str]:
    # Initialize tracking variables
    reasoning_steps = []
    visited_entities = set()
    
    # Step 1: Initial Topic Entities
    topic_entities = self.identify_topic_entities(query)
    visited_entities.update(topic_entities)
    
    # Initial document retrieval
    initial_contexts = self.knowledge_graph.search_entity_documents(topic_entities, query)
    
    # Iterative exploration loop
    for depth in range(self.max_depth):
        # Step 2: Knowledge-guided Graph Search
        for entity_id in topic_entities:
            # Rank relations by relevance to query
            ranked_relations = self.knowledge_graph.prune_relations(entity_id, query)
            
            # Explore connected entities through top relations
            for relation in ranked_relations[:self.max_width]:
                # Find entities connected through this relation
                related_entity_ids = self.knowledge_graph.get_entity_candidates(
                    relation['entity_id'], relation['relation'], relation['head']
                )
                
                # Track these new entities and relationships
                # ...
        
        # Step 3: Knowledge-guided Context Retrieval
        # Retrieve document contexts for newly discovered entities
        # ...
        
        # Step 4: Context-based Entity Pruning
        # Rank entities based on context relevance
        # ...
        
        # Should we continue exploring or generate an answer?
        # ...