Java Meets AI: Empowering Developers to Build Intelligent Apps
summary
Recipe finder implemented three times using LangChain4j, Spring AI and Semantic Kernel connects GPT‑4 / Azure OpenAI / Ollama, ingests PDF recipes, builds embeddings into a Redis vector store, and demos retrieval‑augmented generation plus tool calling and structured JSON outputs; image generation via DALL·E is noticeably slower. Spring auto‑configuration makes setup easiest, LangChain4j offers cross‑framework flexibility, Semantic Kernel gives fine control, and the demos successfully return relevant recipes (e.g., cheese + bacon).
AI-written summary · about
chapters (13)
- 0:00Introduction and speakers
- 1:44What is generative AI
- 4:14Why Java for AI frameworks
- 6:25Overview of Java AI frameworks
- 9:56Demo of recipe finder app
- 11:26LangChain4j implementation
- 17:27Spring AI implementation
- 21:25Semantic Kernel for Java
- 24:20Framework comparison
- 28:51Advanced AI features overview
- 34:49Tool calling and RAG implementation
- 45:26Agentic AI and MCP protocol
- 49:21Summary and recommendations
related talks
44mJava Meets AI: Empowering Spring Developers to Build Intelligent Apps
53mJava meets AI: Build LLM-Powered Apps with LangChain4j
02h 49mJava meets AI: Build LLM-Powered Apps with LangChain4j
55mAI in Java? Meet LangChain4j and Build Intelligent Services
01h 03mBuilding AI powered Applications with Java
47mJava Meets AI: Integrating LLMs into Your Applications with LangChain4j
46mGenerative AI and Java
42mBuilding AI Powered Applications with Java