Taking GenAI Applications to the Next Level with GraphRAG
While the established RAG architectures allow for more focused retrieval, they struggle with necessary context and global meaning. Relationships between entities in documents spanning across documents are hard to represent in vector space. GraphRAG was proposed and evolved by teams at Microsoft, Neo4j, and others to augment LLMs where the Retrieval path includes a Knowledge Graph like below:
GraphRAG Architecture:
It’s also possible to construct a property graph as a knowledge base and use it for much better responses to questions therefore reducing hallucinations and validating answers via constraints.
GraphRAG adds the following components to the Generative AI workflow.
- Represent knowledge as a graph. The graph can be created from an existing knowledge graph or even generated by LLMs through entity extraction, document chunking, and enrichment from metadata.
- Multi-hop reasoning over relationships: the graph naturally allows traversal and graph queries, using the long-term and cross-cutting connections with logical properties.
- Graph structure provides context for queries via node and relationship properties resulting in higher accuracy in retrieved responses.
- Structured and unstructured data is naturally combined. In fact a traditional RAG can augment a GraphRAG. Embeddings could be stored directly in the nodes.
- Explainability improves by showing the paths traversed in the agentic flow.
GraphRAG use cases are numerous, for example:
- Medical Diagnosis and Treatment – codifying symptoms and illness lifecycles follows the natural decision trees from data-driven medicine. Organizing patient data as a graph is already standard in epidemiology.
- Telecommunications - in telecom, graph technology is used for managing and querying challenging network structures.
- Retail - retail businesses knowledge graphs use real-time product recommendation engines which allow for enhanced customer experience personalization.
- Financial Analysis – financial institutions already are major users of knowledge graphs. Events and news are regularly extracted from live streams, aligned with monetary signals, and searched for alpha and compliance. It becomes even more important to answer crucial questions regarding fraud or risk based on the ground truth.
- Sales and Marketing – aligning multiple data points of interest, engagement, and social proximity, it becomes easier to make meaningful offerings and nourish organic relationships.
Further reading, courses, and tools for testing Knowledge Graphs with LLMs can be found at The GenAI Ecosystem. For additional research on the newest GraphRAG developments with Neo4j and its partners LangChain, Llama Index, as well as upcoming collaborations with Pinecone, Zilliz and more can be found in the Neo4j GraphRAG Manifesto, Microsoft GraphRAG project, and their bibliographies.