Kelvin Lawrence

GraphRAG combines knowledge graphs with RAG to produce explainable responses grounded in semantic relationships between concepts, entities, and underlying content. Agentic GraphRAG takes this further, allowing agents to autonomously decide which GraphRAG tools to use to achieve information goals.

Graph-based agentic reasoning empowers AI systems to dynamically interact with the rich context and relationships in an Amazon Neptune graph, enabling intelligent, flexible, and scalable AI solutions that extract valuable insights from complex, interconnected data.

In this hands-on workshop build powerful generative AI applications by combining the capabilities of Amazon Neptune, generative AI, LLMs, and Model Context Protocol (MCP).

Retrieval Augmented Generation (RAG) applications use the power of generative AI to analyze private datasets. GraphRAG combines knowledge graphs with RAG to produce explainable responses that are grounded in the semantic relationships between concepts, entities, and the underlying content.

The emergence of generative AI has also seen the rise of innovative new graph workloads like GraphRAG and natural language querying. In this chalk talk, learn how to deploy these new workload types on Amazon Neptune.

Amazon Neptune is a fast, reliable, and fully managed graph database service that makes it easier to build and run applications that work with highly connected datasets.

Use cases such as real-time recommendation engines demand high performance. When performance is your top priority, model optimizations can be very important to achieving your goals.

Amazon Neptune is a fast, reliable, and fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. In this workshop, get hands-on experience and learn how to use Amazon Neptune to create and load a connected graph dataset, write connected graph queries with Gremlin and openCypher, and generate rich visualizations with your...

In this session, discover how AWS serverless databases automatically adjust database capacity based on application demand. Learn how you pay only for the capacity your application consumes and how you can save up to 90 percent of your database costs compared to the cost of provisioning capacity for peak load.

Graph databases have many uses including ways to detect fraud, discover security risks, and store data lineage across disparate datasets. Building such applications can require an iterative approach, and many developers prefer to start this process on their local workstations.