LLMs need to connect to the real world. LangChain4j tools, combined with Apache Camel, make this easy. Camel provides robust integration, connecting your LLM to any service or API. This lets your AI interact with databases, queues, and more, creating truly powerful applications. We’ll explore this powerful combination and its potential.

Setting Up the Development Environment

• Ollama: Provides a way to run large language models (LLMs) locally. You can run many models, such as LLama3, Mistral, CodeLlama, and many others on your machine, with full CPU and GPU support.
• Visual Studio Code: With Kaoto, Java, and Quarkus plugins installed.
• OpenJDK 21
• Maven
• Quarkus 3.17
• Quarkus Dev Services: A feature of Quarkus that simplifies the development and testing of applications the development and testing of applications that rely on external services such as databases, messaging systems, and other resources.

You can download the complete code at the following GitHub repo.

https://dzone.com/articles/powering-llms-with-apache-camel-and-langchain4j