// 1. Initialize the model pointing to your local Ollama instance OllamaChatModel = OllamaChatModel.builder() .baseUrl( "http://localhost:11434" ) .modelName( "llama3.2:1b" ) .build(); // 2. Generate a response .generate( "Explain how Java works with Ollama." ); System.out.println( "AI Response: " ); } } Use code with caution. Copied to clipboard 4. Advanced Feature: RAG (Talk to Documents) To build a "complete" professional feature, implement Retrieval-Augmented Generation (RAG) to let the AI answer questions based on your local files: Document Loading : Split local text or PDF files into chunks. Embeddings : Use Ollama’s /api/embeddings endpoint to convert text into vectors. Vector Store : Store these vectors in a local database like or an in-memory store for retrieval during chat. 5. Alternative: Spring Boot Integration If you are building a web application, use to expose the feature as a REST API: Getting Started with Ollama, Llama 3.1 and Spring AI 30 Jul 2024 —
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 ollamac java work
However, this approach is complex. You must manage memory, threads, and tokenization manually. Most developers stick with the HTTP API unless they are building ultra-low-latency systems. Copied to clipboard 4
The "Ollamac" framework was a beast of its own making. Built on the spine of the JVM, it was designed to ingest petabytes of human interaction and spit out "logic." But lately, the logic felt... heavy. Vector Store : Store these vectors in a
to bridge the gap between Java's structured environment and Ollama's local LLM serving. Key Libraries for Java Integration
The codebase is organized into the following modules: