Applications and use cases of graph RAG in LLMs
Graph-based RAG can be particularly effective in various applications:
- Question-answering over knowledge graphs
- Personalized recommendation systems
- Scientific literature analysis
- Drug discovery and biomedical research
- Social network analysis
Here’s an example of how graph RAG could be used for a recommendation system:
class RecommendationGraphRAG(GenerativeGraphRAG): def get_recommendations( self, user_id: str, num_recommendations: int = 5 ) -> List[str]: user_node = self.retrieve(f"User {user_id}", k=1)[0] user_interests = self.graph.nodes[user_node]. get('interests', []) ...