Breaktime Tech Talks

Ep60: Langchain4j & Neo4j Integration Breakthroughs + The Decade of Agents in AI

Welcome to Breaktime Tech Talks! In this episode, dive into the latest updates and challenges in the world of developer tools, AI, and graph databases. 
Episode Highlights:
Overcoming technical hurdles with Langchain4j and Neo4j, including the new support for read-only Neo4j databases in vector indexing (Github feature pull request).
Navigating versioning headaches and framework differences between Spring AI and Quarkus for AI-powered applications.
Lessons learned from hands-on work with Neo4j GraphAcademy courses (GraphAcademy GenAI Fundamentals), including AI and knowledge graphs.
Key takeaways from the Andrej Karpathy interview (YouTube interview link), including:
The strengths and limitations of large language models (LLMs) for developers.
The concept of the “decade of agents” and how agents are shaping the future tech stack.
The importance of teaching as a way to deepen technical understanding.
Upcoming events and workshops:
Neo4j Fundamentals & GenAI hands-on workshop (learn more about workshop) – December 11th, virtual and free.
GraphRAG Fundamentals course on O’Reilly (course details) – December 18th.
NODES 2025 conference session recordings now available (full YouTube playlist).

Ep59: NODES 2025 Highlights + Solving Graph Problems with Cypher 25

In this episode:
Recap of NODES 2025 and standout sessions
How AI and music graphs are shaping new tech (featuring Luanne Misquitta’s talk)
Exploring RushDB: open source tools for graph data
Developer advocacy in the classroom: inspiring the next generation
Updates on Spring AI, Langchain4j, and upcoming workshops
Blog post on new Aura Fundamentals course
Solving tough graph problems with Cypher 25
Resources Mentioned:
NODES 2025 playlist (only keynotes at this time)
Luanne Misquitta’s Music Graph session
RushDB session by Artemiy Vereshchinskiy
Langchain4j read only db issue (solved!)
Neo4j Graph Academy Aura Fundamentals blog post
Solve Hard Problems with Cypher 25 blog post
Advent of Code (2025)
Thanks for listening!

Ep58: Hybrid Search Headaches + GraphRAG as MCP Server

In this episode of Breaktime Tech Talks, dive into the real-world challenges and discoveries from my recent work with Langchain4j, Quarkus, and Neo4j. If you’re a developer navigating the evolving landscape of AI, vector search, and graph databases, this episode is packed with practical insights and lessons learned.
Highlights:
Struggles with configuring hybrid search (vector + graph retrieval) in Langchain4j and Quarkus
Pain of setting up Neo4j vector stores, especially for read-only databases
Data importer docs difference (standalone vs Aura)
Why current frameworks make it hard to customize retrieval workflows
Discovery of Neo4j’s MCP Cypher server for vector search as a tool
Blog post on implementing GraphRAG retrievers as an MCP server for reusable, agentic applications
Updates on the GraphRAG Fundamentals online course and the upcoming NODES 2025 conference
My new new Java book project
Tune in for practical advice, honest roadblocks, and new ideas for building smarter, more flexible developer tools!

Ep57: Developer Advocacy Realities + Conditional Logic in Cypher

In this episode of Breaktime Tech Talks, I share an inside look into developer advocacy, discuss the highs and lows of the role, and review new features in the Cypher query language.
Highlights:
🔎What it’s really like to be a developer advocate: the good, the bad, and the “meh”
🧗🏼‍♀️Common challenges: overwhelm, travel fatigue, balancing diverse responsibilities, and learning to say “no”
🏢Why developer advocacy is often a “departmental orphan” and how that brings unique value
🏆The rewarding aspects: variety, constant learning, connecting with the developer community, playing to your strengths, and prioritizing high-impact work
👩🏽‍💻Updates on Jennifer’s current projects, including work on Spring AI Advisors and an upcoming conference appearance
⚙️A deep dive into Christoffer Bergman’s blog post on Cypher Conditional Queries
 🎊What’s new in Cypher 25: the WHEN THEN ELSE syntax and how it improves query readability and maintenance
Every tech role has its ups and downs, but I've found my place. Don’t miss my insights on Cypher’s latest features and stay tuned for more updates on my projects and events. Thanks for listening, and happy coding!

Ep56: Java and Langchain4j Releases + GraphRAG with Langchain4j

In this episode of Breaktime Tech Talks, we focus on frameworks, libraries, and integrations that streamline workflows and enable more powerful applications.
Key Technical Topics Covered:
Releases! Java 25 and Langchain4j 1.5
Spring Initializr Java version default from 17 to 21
New blog post! Spring AI with MCP text-to-cypher
Generating Ollama embeddings for Neo4j (Cypher vs APOC)
Spring AI advisors (QA advisor and RAG advisor)
NODES 2025 - free, online technical event!
Content: Integrating Neo4j with Langchain4j for GraphRAG Vector Stores and Retrievers - GraphRAG with Langchain4j and Neo4j in a Spring app

Ep55: Demystifying MCP + Future of Vibe Coding and RAG

Explore the latest challenge with Neo4j vector indexes, demystify Model Context Protocol (MCP), and hear insights on vibe coding and Retrieval-Augmented Generation (RAG).
What's Inside:
Confusion around Neo4j vector indexes - models and dimensions
Why knowing the embedding model matters for vector similarity search
The limitations of current Neo4j vector index metadata
What is Model Context Protocol (MCP) and why it matters for generative AI
Real-world analogies for understanding MCP (microservices, snack choices, Docker containers)
The power of MCP servers for secure, modular data access
Article highlight: “From Gimmick to Game Changer – Vibe Coding Myths Debunked”
How AI coding tools and generative AI are lowering barriers for developers and business users
Risk mitigation vs. risk avoidance in adopting new technologies
YouTube livestream: “RAG Was Fine, Until It Wasn’t” – lessons from Neo4j Graph Academy’s evolution
The importance of focusing on goals over syntax in development
Links & Resources:
Neo4j vector index documentation
Neo4j MCP server information
From Gimmick to Game Changer – Vibe Coding Myths Debunked (article by Michael Hunger)
RAG Was Fine, Until It Wasn’t (YouTube livestream)
Thanks for listening! If you enjoyed this episode, please subscribe, share, and leave a review. Happy coding!

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

Ep53: Language Models for Data Tasks + MCP Journey Begins

In this episode of Breaktime Tech Talks, I delve into my recent experiences with Model Context Protocol (MCP) and Large Language Models, specifically Claude. First, I share my experiment using an LLM to clean up flat files. Then, my journey with MCP began integrating a Neo4j MCP server with Claude, highlighting the practical benefits and challenges faced with an anecdote on one particular incident where the LLM blended facts. It's also crucial to have clean data sets, but this is rather challenging. To round us out, I summarize an article about the recently released Neo4j data modeling MCP server and its functionality. Join me as I navigates these intriguing tech explorations and sift out the practical takeaways.
 
00:00 Introduction to Breaktime Tech Talks
00:48 Exploring Large Language Models for Flat File Cleanup
03:01 Diving into MCP Exploration
05:02 Challenges with Large Language Models
08:33 Data Set Challenges and Solutions
10:05 Highlight: Neo4j Data Modeling MCP Server
12:11 Conclusion and Future Directions

Ep52: Enhancing AI with Spring Advisors + GraphRAG Python Adventures

In this episode of Breaktime Tech Talks, I share insights from my recent work, including a successful GraphRAG workshop and breakthroughs in utilizing Spring AI advisors for vector search and generative AI - check out code in my Github repository for QuestionAnswerAdvisor branch and custom advisors branch. I discuss my methods for integrating default and custom advisors, including coding details and implementation challenges. I also cover my exploration of Neo4j's GraphRAG Python package, highlighting its components and the learning curve. I give updates on my upcoming projects, advocacy activities, and my experience with new developer tools like Claude code. Finally, I share a great resource on everything you need to know about GraphRAG.
 
00:00 Introduction to Breaktime Tech Talks
00:37 GraphRAG Workshop and Python Learning
01:27 Spring AI Advisors and Custom Implementations
06:32 GraphRAG Python Package Insights
08:42 Developer Advocacy Updates
10:15 Exploring AI Tools and Learning Approaches
11:39 GraphRAG.com Resource Overview
12:53 Conclusion and Upcoming Projects

Ep51: Exploring AI Agents + Agentic GraphRAG in Java

In this episode, I delve into the world of agents, discussing my experience with Spring AI tool calling. I share my approach to vector search and graph retrieval tools, address JSON deserialization, and avoid manual boilerplate - the code of which is all available in a Github repository branch. Plus, 1.0 updates to the main branch of the repository using traditional/manual GraphRAG. I wrap up with a recent content piece by Christoffer Bergman from Neo4j, which explores agentic AI frameworks with Java and Neo4j and the differences between traditional and agentic GraphRAG approaches.
P.S. Don't forget to leave your feedback/suggestions for BTT in this form!
 
00:00 Introduction to Breaktime Tech Talks
00:54 Exploring Spring AI Tool Calling
01:20 Understanding Agentic Frameworks
02:13 Hands-On with Vector Search and Graph Retrieval
02:36 Challenges and Solutions in Tool Functionality
04:02 Updates and Future Plans
05:01 Agentic AI with Java and Neo4j
08:06 Conclusion and Recap