Artwork

Content provided by jmhreif. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by jmhreif or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Player FM - Podcast App
Go offline with the Player FM app!

Ep54: Spring AI Integrations + Real-World RAG Challenges

13:10
 
Share
 

Manage episode 503206916 series 3579839
Content provided by jmhreif. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by jmhreif or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

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:

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

  continue reading

55 episodes

Artwork
iconShare
 
Manage episode 503206916 series 3579839
Content provided by jmhreif. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by jmhreif or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

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:

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

  continue reading

55 episodes

All episodes

×
 
Loading …

Welcome to Player FM!

Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.

 

Quick Reference Guide

Copyright 2025 | Privacy Policy | Terms of Service | | Copyright
Listen to this show while you explore
Play