GraphRAG Innovations: The Future of Intelligent Applications

Welcome to GraphRAG Innovations: The Future of Intelligent Applications, an exciting meetup dedicated to exploring the forefront of AI technology. This event is perfect for AI enthusiasts, researchers, and professionals who are eager to delve into the groundbreaking integration of Retrieval-Augmented Generation (RAG) models with Knowledge Graphs to create more intelligent and context-aware applications.

The agenda of the evening would be:

• 5pm: Doors open
• 5.30pm: Announcements and welcome
• 5.40pm: 1st Talk
• 6.10pm: 2nd Talk
• 6:40pm: Networking
• 7.10pm: Doors close

Speakers:
🎤 Dr. Jim Webber, Chief Data Scientist @Neo4j
Talk topic: Research @ Neo4j
Bio: Dr. Jim Webber is Neo4j’s Chief Scientist and Visiting Professor at Newcastle University. At Neo4j, Jim leads the Systems Research Group, working on a variety of database systems research topics including query languages and runtimes, scale, and fault-tolerance. He also co-authored several books on graph technology including Graph Databases - 1st and 2nd Editions (O’Reilly), Graph Databases for Dummies (Wiley), and Building Knowledge Graphs (O’Reilly).

Prior to Neo4j, Jim worked on fault-tolerant distributed systems. First at Newcastle University startup Arjuna and then for a variety of clients for global consulting firm ThoughtWorks. Along the way Jim co-authored the distrubuted systems books REST in Practice (O’Reilly) and Developing Enterprise Web Services - An Architect’s Guide (Prentice-Hall).

🎤Alison Cossette, Developer Advocate at Neo4j
Topic: Practical GraphRAG
Talk Description: 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.

Bio: Alison Cossette is a Data Science Strategist, Educator, and Developer Advocate at Neo4j, specializing in Graph Data Science. With a strong background in AI and ethical AI practices, Alison bridges complex data concepts with real-world applications. She did Master’s studies at Northwestern University and has conducted research with Stanford's Human-Computer Interaction Crowd Research Collective. Passionate about responsible AI, Alison engages with professionals, policymakers, and the public to promote ethical AI development. She also hosts a podcast, driving innovation and education in data science and AI, making her a respected figure in the field.