Breaktime Tech Talks

Ep71: GraphRAG Learnings + Langchain4j Apps for Production

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This week, I share hard-won lessons from building a GraphRAG application with Neo4j in Python, plus standout tips from Lize Raes's Devoxx Belgium talk on taking Langchain4j apps to production.

GraphRAG with Neo4j

  • Built a Python GraphRAG app using the Neo4j GraphRAG package — knowledge graph construction, retrievers (vector, graph, text-to-cypher), and agentic orchestration
  • Key lesson: don't let the LLM decide your entire data model. Providing node types, relationship types, and patterns as boundaries dramatically improves results
  • Expect iteration — retrieval testing will send you back to refine your KG construction
  • Github code: Neo4j GraphRAG Python package

    • Langchain4j for Production (Lize Raes, Devoxx Belgium)

    • Wrap RAG as an agent tool for multi-call retrieval instead of single-shot pipelines
    • Filter available tools programmatically by domain to keep agents focused
    • Wire sub-agents as @Tool for clean multi-agent orchestration
    • Use immediate responses to skip the LLM summarization hop — saves tokens and latency
    • 13-step walkthrough for production-grade agentic systems
    • YouTube link: Level Up Your Langchain4j Apps for Production (Lize Raes, Devoxx Belgium 2025)
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      Breaktime Tech TalksBy jmhreif
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