Building AI powered Applications with Java

on YouTube ↗01h 03m

" summary

Shows a concrete Java RAG pipeline using LangChain4j (not Spring AI for the demo), GPT‑4‑mini for embeddings, and an Oracle Convergent Database as a vector store started via testcontainers. Demo indexes 397 Asciidoc/txt files into ~2,583 embeddings, stores vectors in Oracle, and runs retrieval that sometimes returns correct policy answers but remains nondeterministic. Includes practical notes: chunking choices, parser pitfalls, a 900MB Oracle image that starts in ~10s versus a 9GB image at ~3min, and mention of NCP/MCP and Spring AI Java SDK.

AI-written summary · about

" chapters (6)

1. 0:00Introduction and setup
2. 11:13Why AI matters now
3. 22:19Oracle database for AI
4. 33:15Demo with LangChain4j
5. 57:21Future of AI in Java
6. 1:01:17Closing and resources

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