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dev.langchain4j langchain4j-ollama 0.33.0 Use code with caution. For ( build.gradle ): implementation 'dev.langchain4j:langchain4j-ollama:0.33.0' Use code with caution. 2. Synchronous Chat Generation

If you want to avoid third-party libraries, you can interact with Ollama using Java’s built-in HttpClient . This approach is ideal for simple automation scripts or microservices with strict dependency constraints.

: A Java version of the popular LangChain framework that allows you to build complex AI pipelines, including RAG (Retrieval-Augmented Generation) using Ollama as the local LLM backend. ollamac java work

LangChain4j is the de‑facto LLM abstraction library for the whole Java ecosystem. It works with Spring Boot, Quarkus, MicroProfile, and even plain Java SE.

Combining Ollama and Java provides software engineers with an ideal toolkit to build private, cost-effective, high-performance AI applications. Whether you use the robust orchestration of LangChain4j, the seamless ecosystem of Spring AI, or the direct utility of Ollama4j, you retain absolute control over your data and execution layer. Synchronous Chat Generation If you want to avoid

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.

// Capture the assistant's response as it arrives and add to history return responseStream.doOnComplete(() -> // This is a simplified example; in a real app you would accumulate the full response ); LangChain4j is the de‑facto LLM abstraction library for

<dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-webflux</artifactId> </dependency> <dependency> <groupId>org.springframework.ai</groupId> <artifactId>spring-ai-ollama-spring-boot-starter</artifactId> <version>1.0.0-M6</version> </dependency>

Integrating —a tool for running Large Language Models (LLMs) locally —into Java development enables developers to build AI-powered applications without relying on cloud-based APIs like OpenAI . This local setup ensures data privacy, offline functionality, and cost efficiency .