Breaktime Tech Talks

Ep54: Spring AI Integrations + Real-World RAG Challenges

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Hear my latest hands-on experiences and lessons learned from the world of AI, graph databases, and developer tooling.

What’s Inside:

  • The difference between sparse and dense vectors, and how Neo4j handles them in real-world scenarios.
  • First impressions and practical tips on integrating Spring AI MCP with Neo4j’s MCP servers—including what worked, what didn’t, and how to piece together documentation from multiple sources.
  • Working with Pinecone and Neo4j for vector RAG (Retrieval-Augmented Generation) and graph RAG, plus the challenges of mapping results back to Java entities.
  • Reflections on the limitations of keyword search versus the power of contextual, conversational AI queries—using a book recommendation system demo.
  • Highlights from the article “Your RAG Pipeline is Lying with Confidence—Here’s How I Gave It a Brain with Neo4j”, including strategies for smarter chunking, avoiding semantic drift, and improving retrieval accuracy.

    • Links & Resources:

    • Neo4j MCP Cypher server repository
    • Spring AI MCP client
    • Your RAG Pipeline is Lying with Confidence
    • Jennifer’s Goodreads demo app

      • Thanks for listening! If you enjoyed this episode, please subscribe, share, and leave a review. Happy coding!

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      Breaktime Tech TalksBy jmhreif
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