Building Intelligent Enterprises: AI-Powered Java with LangChain4j & CDI
summary
Claim: add LLM-driven features to existing Java enterprise stacks without a Python migration. Uses langchain4j-cdi on Jakarta EE / MicroProfile with CDI to inject an AI service, switch providers via config, and plug in RAG with vector store and embeddings. Demonstrates a working Liberty car-booking app calling tools (book, get details, cancel) that enforces cancellation rules, runs locally against Llama 3.1, and shows telemetry and tracing. Notes: supports 20+ LLMs, 30+ vector stores, ~400 contributors; adds retries, timeouts and function-calling.
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chapters (9)
- 0:00Introduction to Java and AI
- 2:24Jakarta EE and AI landscape
- 5:34AI enhanced Java applications
- 16:06Understanding AI agents
- 21:13Introduction to LangChain4j
- 24:25LangChain4j-CDI overview
- 27:21Implementing AI in Jakarta EE
- 33:50Live code demonstration
- 43:04Conclusion and community
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