Next-Level Features of LangChain4j for Production-Ready AI Applications
summary
LangChain4j extends to production with Quarkus integration, guardrails, observability to Grafana, and evaluation tooling; demonstrated by a drug‑discovery app that uses OpenAI GPT‑4 and Claude plus an AlphaFold call (AlphaProteo was stubbed) to design antibodies for GBM, finds only one existing antibody in the dataset, and scores/filters retrieved snippets with a reranking model keeping the top 5 fragments. The move from an LLM-driven flow to a coded state machine fixes nondeterminism; Advanced RAG adds query routing, compression, and aggregators; MCP tool servers are supported.
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chapters (24)
- 0:00LangChain4j production features
- 1:48Talk overview and roadmap
- 2:54Speakers and project background
- 3:35AI agents for research discovery
- 7:35Antibody discovery demo app
- 11:52Live AI research tool demo
- 14:21Code deep dive: AI Services and RAG
- 16:53RAG limitations and improvements
- 17:35Advanced RAG architecture
- 20:45Advanced RAG code walkthrough
- 24:00State machines vs LLM workflows
- 27:49State machine code
- 29:58State machine demo run
- 36:24Model Context Protocol integrations
- 37:08Safety guardrails and moderation
- 38:40Input guardrails code
- 40:06Data privacy and pricing
- 41:10Token consumption benchmarking
- 41:52Observability with Grafana
- 42:56Evaluating AI applications
- 43:38Testing with AI Scorer
- 45:46User feedback and improvement
- 46:29Future features roadmap
- 46:50Q&A
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