private static String extractResponse(String json) // Very naive – use Gson or Jackson in real code int start = json.indexOf("\"response\":\"") + 11; int end = json.indexOf("\"", start); return json.substring(start, end);
Ollama is an open-source tool designed to get large language models—such as Llama 3, Mistral, Gemma 2, and Phi—running locally on your machine. It manages the complexities of model loading, GPU acceleration, and interaction, acting as a background service that provides a simple API for applications. Key Benefits of Running Ollama Locally: Data never leaves your machine. Cost: No API token fees or usage charges. Offline Access: Run models without an internet connection.
+---------------------------+ +-----------------------+ | Java Application | REST / HTTP | Ollama Server | | (Spring AI / Ollama4j) | ------------>| (Localhost:11434) | +---------------------------+ +-----------------------+ | v +---------------------+ | Local Model (Llama) | +---------------------+
dev.langchain4j langchain4j-ollama 0.31.0 Use code with caution.
Could be a typo for:
LangChain4j provides a clean builder pattern to connect to the local server (defaulting to http://localhost:11434 ).
# On macOS/Linux/WSL curl -fsSL https://ollama.com/install.sh | sh
OllamaChatModel model = OllamaChatModel.builder() .baseUrl("http://localhost:11434") .modelName("llama3:8b") .temperature(0.7) .build();
Tool calling enables the model to request the execution of a specific function. For example, in a customer service chatbot, the model might identify a user's intent to check an order status and respond by asking your code to call a getOrderStatus(orderId) API. The model returns a structured JSON object specifying the tool to use and its arguments. Spring AI provides robust abstractions for simplifying tool calling. ollamac java work
: A dedicated Java library that wraps the Ollama REST API. It allows you to "ping" the server and manage models directly through Java objects.
@PostMapping("/chat/stream") public Flux<String> chatStream(@RequestBody String message) return chatClient.prompt() .user(message) .stream() .content();
If you see a JSON response with a "response" field, you are ready to move to Java.
To work with in Java , you can use several specialized libraries that bridge the gap between Java applications and the local Ollama REST API. Popular Java Libraries for Ollama Cost: No API token fees or usage charges
What are you running this on? (Apple Silicon Mac, Intel, dedicated GPU?)
The lowest‑level but most flexible approach. You send HTTP requests to Ollama’s endpoints and parse JSON responses. This is ideal when you need full control or when you cannot add extra dependencies.
You can build a Java application that reads your local PDF documentation, stores embeddings in a local vector database (like Chroma or Milvus), and uses Ollama to answer questions based only on your private files. Intelligent Unit Test Generation