Ep54: Spring AI Integrations + Real-World RAG Challenges

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

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