GraphRAG with Neo4j and Diffbot: Enhancing AI Accuracy

In the video ‘Reliable Graph RAG with Neo4j and Diffbot,’ the Diffbot team demonstrates the development of a GraphRAG system for real-time news monitoring using Diffbot’s APIs and Neo4j graph database. The video begins with an introduction to GraphRAG, which allows large language models (LLMs) to navigate and retrieve information from structured data and knowledge graphs. Unlike traditional vector-based retrieval augmented generation (RAG) systems, GraphRAG leverages knowledge graphs to map entities and relationships, providing more accurate and reliable answers.

The video highlights the limitations of vector-based RAG systems, which rely solely on similarity searches and can miss important context, leading to less reliable responses. Microsoft’s research on GraphRAG shows that using knowledge graphs can better link different data points and provide verifiable sources, thus enhancing the accuracy of answers.

To build a reliable GraphRAG, the team first converts unstructured text data into knowledge graphs with entities and relationships. While LLMs can perform this task, they often struggle with entity resolution, leading to inconsistent and unreliable knowledge graphs. Diffbot’s APIs handle entity resolution by assigning unique identifiers to each entity, ensuring consistency across various sources.

The project demonstration begins with importing news articles related to Nvidia and its developments in LLMs. Using Neo4j, the team shows how the articles are converted into a knowledge graph, enriched with entities and relationships. The system also integrates vector-based searches, storing text embeddings as properties under chunk nodes in the graph database.

The enriched knowledge graph allows for more comprehensive and accurate question answering (Q&A). The video compares answers generated by vector-based RAG and GraphRAG, showing that the latter provides more structured and detailed information by following relationships within the knowledge graph.

The video concludes by encouraging viewers to explore the project on GitHub and experiment with the GraphRAG system. The project showcases the potential of combining vector-based searches with knowledge graphs to improve the accuracy and reliability of information retrieval in real-time applications.