Using knowledge graphs to build GraphRAG applications with Amazon Bedrock and Amazon Neptune

Retrieval Augmented Generation (RAG) combines large language models with external knowledge sources to generate accurate and informative content by leveraging both the language model's contextual understanding and factual data from various sources. The effectiveness of RAG is highly influenced by the choice of data sources, with knowledge graphs being particularly advantageous due to their structured representation of real-world entities and relationships. Knowledge graphs enable efficient information retrieval and integration, allowing RAG to produce responses grounded in factual knowledge. Amazon Bedrock is a managed service that provides access to various high-performing foundation models for building generative AI applications. Using Amazon Bedrock and Amazon Neptune, a GraphRAG solution can be implemented with the LlamaIndex framework, which orchestrates the interaction between large language models and knowledge graphs. This setup involves configuring a Customer 360 knowledge graph in Neptune and integrating it with Bedrock through LlamaIndex for enhanced information retrieval and reasoning. The solution involves setting up the knowledge graph, configuring the components, integrating Neptune with LlamaIndex, and setting up a retriever to perform sub-graph retrievals. Prompt engineering enhances accuracy by converting natural language prompts into Cypher queries for precise retrieval from the knowledge graph. Testing involves generating personalized product recommendations based on user data retrieved from the knowledge graph, showcasing the system's ability to provide tailored responses. Finally, the solution demonstrates the potential of GraphRAG to combine natural language understanding with structured knowledge for generating accurate and informative responses, emphasizing the integration capabilities of Amazon Bedrock and Amazon Neptune in facilitating advanced AI-powered applications.