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Ep54: Spring AI Integrations + Real-World RAG Challenges
Manage episode 503206916 series 3579839
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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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