As a Senior Software Engineer at Red Hat, I specialize in Integration and Open Source technologies. My expertise centers on solving complex challenges in Cloud Native environments. As an active contributor to the Apache Camel community, I focus on advancing integration capabilities within Cloud Native applications, particularly leveraging the Quarkus framework. My work is driven by a passion for pioneering solutions that shape the future of technology.
Engaged in various open source and women in tech communities, I actively participate as a tech speaker, sharing insights and experiences with fellow enthusiasts.
In today's dynamic AI landscape, the seamless integration of Large Language Models (LLMs) into applications is a key focus for developers. While many initiatives have emerged to facilitate the integration of LLMs, the world of Java has seen limited options.
Enter Langchain4j, a powerful library designed to seamlessly integrate Java applications with LLMs. The excitement amplifies Langchain4j into Quarkus, a framework designed for building Cloud-Native applications in Java. Quarkus is tuned for Kubernetes environments boasting faster startup times and reduced memory usage compared to traditional Java applications. When Quarkus meets Langchain4j, the process of building a Java LLM-powered application becomes an enjoyable experience.
In this talk, we’ll delve into building AI applications powered by LLMs, using Quarkus and Langchain4j. We’ll leverage existing features from the ecosystem to create effective strategies for data ingestion. We’ll demonstrate how to seamlessly bring in information from a broader set of resources, with the power of Apache Camel.
Searching for speaker images...