The Data Exchange with Ben Lorica
Supercharging AI with Graphs
Jun 27, 2024
Philip Rathle, CTO of Neo4j, discusses GraphRAG and GQL. Topics include Graph Neural Networks with LLMs, constructing knowledge graphs from various sources, using graphs in AI applications like supply chain risk analysis, benefits in healthcare and customer service, and integrating vector and graph databases for efficient data analysis.
43:58
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Quick takeaways
- GraphRAG combines domain and lexical graphs for enhanced data retrieval.
- GQL standardizes graph query language, promoting wider adoption of graph models.
Deep dives
GraphRag: Bridging Domain Graphs and Lexical Graphs
GraphRag, a hybrid approach combining domain graphs and lexical graphs, is discussed. Synthetic data generation and agentic workflows are identified as key trends. GraphRag involves storing workflows in a graph, enhancing resilience, robustness, and debugging capabilities. Additionally, the generation of synthetic data using graphs to improve accuracy is highlighted as a novel application.
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