Java meets AI: Build LLM-Powered Apps with LangChain4j
summary
Shows concrete recipes: use LangChain4j (OpenAI, Gemini, HuggingFace, Azure, AWS) to return typed POJOs, run local models via Orama, and generate images with DALL·E or Gemini. Demonstrates a 20‑page article summarized to a few minutes of reading, a chatbot that actually cancels a booking in the PG database, and OCR of messy handwriting in five lines of code. Covers RAG with PGVector, function‑calling tools, prompt‑injection guardrails, OpenTelemetry/Prometheus tracing, and the Quarkus extension for live reload and native builds.
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chapters (22)
- 0:00Introduction and presenters
- 1:26Session overview and goals
- 4:37First example: Summarizer application
- 8:52Second example: Chatbot with RAG
- 13:39Third example: Text extraction from image
- 16:59Understanding AI-infused applications
- 24:13LangChain4j vs Quarkus LangChain4j
- 33:51Basic generative AI with LangChain4j
- 44:06Messages and memory management
- 58:18AI Services in LangChain4j
- 1:08:47Quarkus LangChain4j features
- 1:17:21Chat memory and scopes
- 1:28:06Redis-based persistent memory
- 1:35:04Break and observability setup
- 1:36:49Quarkus AI Services observability
- 1:44:47RAG pattern explained
- 1:58:37RAG ingestion and retrieval demo
- 2:07:14Easy RAG in Quarkus
- 2:14:06Function calling and tools
- 2:29:57Image models and multimodal AI
- 2:36:55All-in-one demo and resources
- 2:39:51Q&A session
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