Jakarta EE Meets AI Beyond the Chatbot With LangChain4j
summary
Shows how to add LLM-driven automation into a normal Jakarta EE app using Jakarta EE, LangChain4j, OpenAI and CDI events. Demonstrates an agent that analyzes customer feedback in the background (not a chatbot), then calls backend tools to create business opportunities, tasks or assign offers; live demo creates an opportunity for 100 iPhone 15 cases with a $1,000 estimate and schedules a follow‑up, and assigns a 10% discount for faulty product reports — the flow executes end‑to‑end.
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chapters (8)
- 0:00Introduction and overview
- 1:27Key LLM concepts explained
- 13:09RAG and vector stores
- 16:21Tools and LangChain4j agents
- 21:19Demo setup with Py Starter
- 28:52Code walkthrough and Jakarta EE integration
- 33:14Live demo and results
- 42:09Q&A and resources
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