Graph databases and Retrieval Augmented Generation
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.
