See how Neo4j's industry-leading knowledge graph powers cutting-edge Generative AI (GenAI) applications.
In this demo, we'll explore a real-world use case to demonstrate how Neo4j’s knowledge graph, combined with vector search, enables superior Retrieval Augmented Generation (RAG). Learn how Neo4j helps overcome common GenAI challenges like hallucinations and lack of explainability while improving the relevance and quality of Large Language model (LLM) responses.
With Neo4j, you can:
-Build a grounding knowledge graph from structured and unstructured data
-Use graph pattern matching with vector search to provide deeper domain context, enhancing the relevance and explainability of LLM responses
-Apply advanced graph analytics, such as Graph Data Science, to gain -valuable insights into grounding data, allowing you to continually analyze, monitor, and improve grounding data quality over time.
Resources
Demo Code on GitHub: https://bit.ly/3Tq2b0u
GenAI Developer & Educational Resources: https://bit.ly/3Tpxxo8
More Neo4j GenAI Resources: https://bit.ly/4ajVELA