LLMs, Graph Databases and RAG in the Cloud
We are super excited to organize an evening event on LLMs, Graph Databases and RAG on Google Cloud Platform. Come and join us for this in-person meetup in Austin, Texas. Pizza will be provided.
You will learn about RAG from three veteran speakers who have been presenting about RAG for the past year: Sagar Kewalramani, Generative AI Solution Engineer, Google Cloud, Jason Koo, Neo4j Developer Advocate and Michael Ryan, Neo4j Field Engineer and
Agenda
4:00pm - Open doors & mingling
4:15pm - Intro and Talks
6:00pm - Socializing
6:30pm - Close doors
Talk #1: Augmenting and Grounding LLMs with Information Retrieval
Grounding helps LLMs access additional information beyond their training data to improve responses. Explore Retrieval-Augmented Generation (RAG), a method of enriching prompts with supporting data stored in Vector databases. Discover how this approach improves response quality and context, reducing the necessity for model retraining.
Talk #2: LLMs, KG and RAG
Introduction to Large Language Models (LLMs), Retrieval Augmented Generation (RAG) using Vector Databases, and Knowledge Graphs (KG). In this talk we’ll highlight and demonstrate limitations of each but show how powerful combining all 3 can be.
Talk #3: RAG demo project.
In this talk we will be showcasing how one can take a video off of YouTube and generate a Knowledge Graph from the content in the video using a LLM. Then we will integrate a LLM to be able to query the newly created Knowledge Graph with plain english questions. This will leverage Neo4j’s graph database along with GCP’s VertexAI.
Bios:
Jason Koo, Developer Advocate @ Neo4j
Mobile Developer turned Pythonista, Jason Koo is a Developer Advocate Manager at Neo4j who works with a small but mighty team of Advocates. He enjoys coding late into the night and then passing on his learnings to other developers online, at meetups, conferences, and the occasional street-corner.
Michael Ryan, Solutions Engineer @ Neo4j
Michael received his MS in Operations Research and BS in Mechanical Engineering from Southern Methodist University. At Neo4j, Michael's work spans working with machine learning models, demonstrating the value of graph databases, and enabling people with the skills of how to use a graph database.
Sagar Kewalramani, Generative AI Solution Engineer @ Google Cloud
Sagar is an accomplished Big Data and Machine Learning Architect with a passion for designing innovative solutions that accelerate digital transformation and simplify data management and analytics. At Google Cloud, Sagar serves as a Generative AI Specialist, collaborating with major enterprises to build and scale production grade Generative AI applications.