For the last 14 years, he has been working on the open source Neo4j graph database filling many roles, most recently leading Product Innovation and Developer Strategy.
He especially loves to work with graph-related projects, users, and contributors, his current focus is generative AI, cloud integrations and developer experience.
As a developer Michael enjoys many aspects of programming languages, learning new things every day, participating in exciting and ambitious open source projects and contributing and writing software related books and articles.
Michael spoke at numerous conferences and helped organized several of them. His efforts got him accepted to the JavaChampions program.
Michael helps kids to learn to program by running weekly girls-only coding classes at local schools.
We all know that LLMs hallucinate and RAG can help by providing current, relevant information to the model for generative tasks.
But can we do better than just vector retrievals? A knowledge graph can represent data (and reality) at high fidelity and can make this
rich context available based on the user's questions. But how to turn your text data into graphs data structures?
Here is where the language skills of LLM can help to extract entities and relationships from text, which you then can correlate with sources,
cluster into communities and navigate while answering the questions.
In this talk we will both dive into Microsoft Research's GraphRAG approach as well as run the indexing and search live with Neo4j and LangChain.
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