A Java backend running inside a hospital’s firewall can process patient notes using Ollama + a small model like . The OllamaC integration ensures no data ever leaves the secure network.

curl -N -X POST http://localhost:8080/api/chat/session123 -H "Content-Type: text/plain" -d "What is Project Loom in Java?"

The easiest way for Spring Boot applications. Ollama4j: A dedicated Java wrapper library for Ollama. Approach 1: Spring AI and Ollama

LLMs are resource-heavy. Ensure your development machine has adequate RAM (minimum 16GB for 7B models, 32GB+ for larger models) to prevent the Java JVM and Ollama from competing for system memory.

– Another mature client, used by developers for building Eclipse IDE plugins. The project is actively maintained, and you can track its issue tracker for edge‑case behaviour like tool‑call parsing.

Let’s explore three common integration levels.

import org.springframework.ai.ollama.OllamaChatModel; import org.springframework.web.bind.annotation.GetMapping; import org.springframework.web.bind.annotation.RequestParam; import org.springframework.web.bind.annotation.RestController; import reactor.core.publisher.Flux; @RestController public class AIController private final OllamaChatModel chatModel; public AIController(OllamaChatModel chatModel) this.chatModel = chatModel; // Standard synchronous response @GetMapping("/ai/generate") public String generate(@RequestParam(value = "message") String message) return chatModel.call(message); // Reactive streaming response for real-time UI rendering @GetMapping("/ai/stream") public Flux stream(@RequestParam(value = "message") String message) return chatModel.stream(message); Use code with caution. Method 3: Advanced AI Patterns with LangChain4j

While you can interact with Ollama's native REST API using Java's built-in HttpClient , the standard approach in the industry is to use .