Sometime back, I created a small wrapper called Ollama4j to interact with the Ollama server over the REST API in Java as a side project and made the repository public on GitHub. Over time, the project gained traction, with many fellow Java developers contributing, and it now boasts over 300 stars! 😍

We’ve consistently introduced new features, and when we added the tool-calling capability, the library became incredibly powerful, opening up so many possibilities. With this addition, we could automate numerous tasks using natural language! I wanted to share how to make the most of this functionality.

In this article, we’ll explore how to use Ollama4j, a Java SDK for interacting with Ollama-hosted models, to leverage tool-calling models like Mistral for querying a Couchbase database. The goal is to create a system where you can query your database using natural, conversational language — just like interacting with a virtual assistant. We’ll walk you through the code, explain the key components, and show you how to set up your environment to ensure everything runs smoothly.

Hey there, my fellow Java Developers! 🚀

I am glad to announce the release of Ollama4j, a library that unites Ollama (an LLM manager and runner) and your Java applications! 🌐🚀

👉 GitHub Repository: Ollama4j on GitHub (https://github.com/ollama4j/ollama4j)

🌟 Key Features:

• Easy integration with Ollama, enabling the execution of large language models locally.
• Clean and simple APIs, focused on seamless interaction with Ollama.
• Empowers Java developers to harness the full capabilities of Ollama.
• Provides APIs to perform operations such as listing, pulling, deleting models, and creating custom models.
• Provides APIs to ask questions (generate completions) to the LLMs in synchronous and asynchronous modes.
• Ability to ask questions along with image files or image URLs! 🤩
• Open-source and primed for collaborative contributions from the community!