A comprehensive list of awesome resources for JAVA developers about how to use Large Language Models focused on Spring AI, LangChain4J (no reference to Python LangChain), and Semantic Kernel (for Java). We will not include libraries for deep learning or training models like those from Baeldung site.

Key Points

• Spring AI, LangChain4J, and Semantic Kernel for Java help integrate Large Language Models (LLMs) into Java apps for tasks like chatbots and text summaries.
• Resources include official docs, GitHub repos, articles, and books, all focused on using pre-trained LLMs, not training models.
• Surprisingly, Spring AI has dedicated books, while LangChain4J and Semantic Kernel for Java rely more on online resources.

Spring AI: Easy LLM Integration for Java Developers

Spring AI is a framework from the Spring ecosystem, making it simple for Java developers to add LLM features like chatbots and text generation to their apps. It supports major providers like OpenAI and Google, using familiar Spring Boot tools.

• Official Resources: Start with the Spring AI Reference for detailed guides and the GitHub repo for code and examples.
• Learning Materials: Check out Baeldung's intro for basics and Pluralsight's guide for practical examples.
• Books: Dive into "Mastering Spring AI" (Amazon link) and "Spring AI in Action" (Manning link) for deep insights.

LangChain4J: Unified API for LLMs in Java

LangChain4J is a Java library for building LLM-powered apps like chatbots, offering a unified API for providers like OpenAI and vector stores. It’s great for retrieval-augmented generation (RAG) and integrates with Spring Boot and Quarkus.

• Official Resources: Use the docs for tutorials and the GitHub repo for examples.
• Learning Materials: Read Baeldung's basics and Java Code Geeks' Spring Boot guide for hands-on learning.
• eBook: Explore "Understanding LangChain4j" (eBook link) for practical examples.

Semantic Kernel for Java: AI Orchestration Made Simple

Semantic Kernel for Java, from Microsoft, integrates LLMs like OpenAI into Java apps, focusing on plugin orchestration and planning. It’s ideal for building AI agents with natural language prompts.

• Official Resources: Access the Microsoft Learn docs and the GitHub repo for samples.
• Learning Materials: Check Microsoft's intro blog and updates like GA announcement for insights.