06-14, 13:30–15:00 (Europe/London), Minories
In the era of large language models (LLMs), the integration of external, structured knowledge bases has emerged as a frontier for enhancing AI's textual comprehension and generation capabilities. The Retrieval-Augmented Generation (RAG) architecture represents a pivotal advancement in this domain, particularly when leveraging graph databases to augment LLMs.
This workshop will break down how combining these technologies can make AI not just better at creating text that's both accurate and relevant, but also capable of understanding context like never before. We'll explore the building blocks of RAG—how it uses a 'retriever' to find useful information and a 'generator' to create responses. Graph databases play a crucial role here; they're a type of database that's really good at showing how different pieces of information are connected. This ability makes AI responses more insightful and adaptable to new information. Step by step, we'll walk through how to build AI applications using RAG and graph databases, covering everything from the initial setup and getting the data ready, to fine-tuning how the AI finds and uses information to answer questions or write text. This session is designed to give you the tools to create AI that not only knows more but can also use that knowledge to generate responses that truly understand and reflect the complexity of the world around us.
This tutorial is about enhancing the capabilities of AI in text generation and comprehension by integrating graph databases with Retrieval-Augmented Generation (RAG) technology. It aims to make AI smarter by teaching it how to use a vast web of interconnected information, enabling it to produce responses that are not only more accurate and relevant but also rich in context. The session will cover the basics of RAG—how it retrieves information and generates text—and the benefits of using graph databases, which excel at mapping complex relationships between data points. Participants will learn, in a step-by-step manner, how to build AI applications that leverage these technologies, from setting up and processing data to implementing retrieval mechanisms and integrating them with generative models. The goal is to equip attendees with the knowledge to create AI systems that can deeply understand and interact with the world in a more human-like way.
Please ensure docker is downloaded on your laptop and you have an OpenAI API key that can be used for the duration of this tutorial.
No previous knowledge expected
Richard is an Experienced Senior Software Engineer, who has a lot of experience developing and deploying AI applications.