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
Below is a complete example using Java 11+ HttpClient to send a prompt to the /api/generate endpoint. Note that Ollama streams its response by default; to get a single JSON response, set "stream": false in the payload.
<dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-ollama</artifactId> <version>1.0.0</version> </dependency> ollamac java work
Many of your prompts will be identical or nearly identical. Cache responses aggressively:
@Service public class AIService private final ChatClient chatClient; This downloads the Llama 3 model (approx 4
If you need to build complex AI architectures involving Retrieval-Augmented Generation (RAG), memory management, or AI Agents, is the most specialized library available for Java. 1. Add Dependency Include the dedicated Ollama module in your project:
Spring AI is the official Spring framework for AI integration. It provides a consistent API across different model providers, so you can start with a local Ollama model and later switch to OpenAI or Anthropic with almost no code changes. The Modern Way: Using LangChain4j Below is a
: Used for multi-turn conversations where you need to pass the chat history back to the model. Method 1: The Native Java Approach (No Frameworks)
If you prefer not to use a framework, you can interact with Ollama’s REST API directly using Java 11+ HttpClient .
| Aspect | Ollama (Local) | OpenAI / Cloud API | |----------------------|---------------------------------------------|--------------------------------------------| | | Free (only hardware) | Pay per token; large teams can hit $200k/year | | Latency | 110–300 ms for typical code tasks | 800 ms+ due to network overhead | | Data privacy | Complete – no data leaves your servers | Your prompts are sent to a third party | | Model variety | Llama, Mistral, CodeLlama, DeepSeek, Gemma… | OpenAI’s own models only | | Scaling | Limited by your own hardware | Virtually unlimited via API | | Java integration | REST API / Spring AI / LangChain4j | Also REST API / Spring AI / LangChain4j |
In essence, means: “Using Java to interact with locally running Ollama models, often via a compatibility layer that bridges Java ↔ C ↔ Ollama.”
