tog_04.py

def rank_relations(self, query: str, entity_name: str, relations: List[Dict]) -> List[Dict]:
    """Rank relations by their relevance to the query"""
    relation_info = '\n'.join([
        f"- {rel['relation_name']} (connects {entity_name} to {rel['target_name']})" 
        for rel in relations
    ])
    
    # Craft a prompt that asks the LLM to evaluate relations
    system_prompt = """
    You are an expert knowledge graph reasoner. Your task is to:
    1. Analyze how relations contribute to answering queries
    2. Consider multi-step reasoning paths
    3. Evaluate both direct and indirect relationships
    """
    
    prompt = f"""
    Query: {query}
    Entity: {entity_name}
    Available relations:
    {relation_info}
    
    Analyze these relations for answering the query.
    Return a JSON array of objects with relation_name and score (0-10) fields,
    sorted by score in descending order.
    """
    
    # Get LLM response and parse results
    result = self.generate(prompt, system_prompt)
    ranked_relations = self._parse_relation_rankings(result)
    
    # Enrich and sort relations
    # ...
    
    return enriched_relations