Local inference can take time depending on your hardware hardware. Always configure generous connection and read timeouts on your Java HttpClient or LangChain4j builders to prevent premature TimeoutExceptions .
This downloads the Llama 3 model (approx 4.7GB) to your local drive. Ollama will now host a REST API at http://localhost:11434 . Implementing Ollama in Java: Two Primary Methods 1. The Modern Way: Using LangChain4j
"model": "qwen2.5:7b", "prompt": "%s", "stream": false ollamac java work
Ollama provides:
This is perfect for batch jobs, report generation, or data enrichment pipelines. Local inference can take time depending on your
To use OLLAMAC in your Java project, add the following Maven dependency:
import dev.langchain4j.model.chat.StreamingResponseHandler; import dev.langchain4j.model.ollama.OllamaStreamingChatModel; import dev.langchain4j.model.output.Response; import dev.langchain4j.data.message.AiMessage; public class StreamingExample public static void main(String[] args) OllamaStreamingChatModel model = OllamaStreamingChatModel.builder() .baseUrl("http://localhost:11434") .modelName("llama3") .build(); model.generate("Write a short poem about coding.", new StreamingResponseHandler () @Override public void onNext(String token) System.out.print(token); // Prints tokens in real-time as they arrive @Override public void onComplete(Response response) System.out.println("\n\nStream complete."); @Override public void onError(Throwable error) error.printStackTrace(); ); Use code with caution. 2. Retrieval-Augmented Generation (RAG) Ollama will now host a REST API at http://localhost:11434
This is the most straightforward “OllamaC Java work” – despite the name, it doesn’t use the C bindings.
curl http://localhost:11434/api/generate -d '"model": "llama3", "prompt": "Hello, world!"'
Then in Java: