In this session, we’ll show how to turn SurrealDB into a long-term memory layer for your LLM apps, combining graph and vector data to power richer context, better decisions. We’ll walk through practical patterns and show how SurrealDB collapses graph, vector, and relational data into a single memory substrate for next-gen AI.

In this session, we’ll show how to turn SurrealDB into a long-term memory layer for your LLM apps, combining graph and vector data to power richer context, better decisions. We’ll walk through practical patterns and show how SurrealDB collapses graph, vector, and relational data into a single memory substrate for next-gen AI.

Learn how to turn SurrealDB into a long-term memory layer for your LLM apps by combining graph data and vector embeddings to power richer context and better decisions. Store persistent memories with graph-linked facts; perform similarity search and structured reasoning in one query; use vector embeddings and graph hops inside SurrealDB. This session walks through practical patterns and demonstrates how SurrealDB collapses graph, vector, and relational data into a single memory substrate for next-gen AI.

Hands-on learning on building and evaluating generative AI solutions with LLMs responsibly at scale. Learn to create visual executable flows linking LLMs, vector embeddings, prompts, and Python tools; evaluate performance metrics and responsible AI issues such as groundedness, hallucinations, and relevance. Pre-requisites: Basic understanding of Python.

Since the release of ChatGPT late last year, the world has finally embraced vector embeddings and many organisations (from hedge funds to giant retailers) have been experimenting with vector databases. This is because vector embeddings, a component at the heart of large language models, open-up the ability to not only compress information but also to drastically transform search and knowledge retrieval. In this session we will put a spotlight on the embedding revolution that has taken over natural language processing, computer vision, network science and explain how enterprises can build better systems to understand, interact with, and sell to their customers.