You can use Java to index local documents (PDFs, text files), create embeddings using an Ollama embedding model, and query them locally.
One of the most powerful features of Spring AI is its effortless support for , which delivers tokens to the user as they're generated, providing a real-time feel. This is particularly valuable for chat applications.
was a ghost. He lived in the "Ollamac" project—a code-named initiative meant to bridge the gap between Large Language Models and enterprise Java environments. It was supposed to be a tool for efficiency, but for Elias, it had become a cathedral. ollamac java work
Java remains a dominant language in enterprise environments, yet modern LLM integration has largely focused on Python. Ollama simplifies running LLMs locally, but lacks an official Java client. This gap motivated the development of – a lightweight, reactive Java client for Ollama’s REST API. This paper documents the design choices, implementation challenges, and performance benchmarks of OllamaC.
Before writing code, ensure your development machine is ready. You can use Java to index local documents
: A lightweight client library designed for straightforward programmatic interaction, including streaming completion responses. Core Capabilities for Java Workflows
<dependency> <groupId>org.springframework.ai</groupId> <artifactId>spring-ai-ollama-spring-boot-starter</artifactId> <version>1.0.0-M6</version> </dependency> was a ghost
<dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-ollama</artifactId> <version>1.6.0</version> </dependency>