Breaktime Tech Talks

Hear about my hard-won lessons from loading a large-scale book dataset into Neo4j with Ollama embeddings, plus a preview of exciting new vector search features. Highlights: Data Loading Battle Stories Fixing Ollama OpenAI endpoint issues (drop the /v1 suffix!) Choosing embedding models with adequate context windows (nomic-embed-text: 8,192 tokens) Optimizing batch sizes and memory configuration Using EXPLAIN to identify and eliminate Cypher eager operations Error handling with ON ERROR CONTINUE for partial loads (achieved 83% coverage) Neo4j 2026.01 Preview: Vector Search with Filters Three new approaches that combine vector search with Cypher filtering in a single query: Vector Search + Keyword Filters Cypher After Vector (post-filtering GraphRAG) Cypher Before Vector (pre-filtering on subgraphs) No more two-step application logic for Graph RAG! Context Graph demo app: Level of detail and perspectives you can view of the context graph and interactions with agents Event I will be at Jfokus in Stockholm next week!

This week has been a whirlwind. From starting a new RAG project to getting involved in other community events, there is so much to learn and do. This week had the following highlights: 🎤 Glasgow Meetup Adventures Navigating venue challenges, DJ booth speaking setups, and live coding without a mic stand—lessons in developer advocacy resilience. 🔍 RAG Experimentation Working with Quarkus to ingest unstructured data into Neo4j. Exploring filtering strategies and data model alignment for better retrieval. 💡 Live Interaction Tracer Combining naive RAG with a graph-based interaction tracer—early progress on a promising approach. 🧠 Context Graphs Deep Dive Why context graphs matter for AI: documenting the "how" and "why" behind data decisions, not just snapshots in time. Perfect for providing business logic and tacit knowledge to AI systems. Resources Hands-on with Context Graphs and Neo4j by William Lyon William Lyon's podcast episode (previous month) Context Graphs demo application Lots of 2026 projects kicking off—stay tuned for updates on RAG experiments, context graph implementations, and upcoming events!

Welcome back to Breaktime Tech Talks for 2026! In this episode, dive into the technical challenges I faced with GenAI procedure migrations, and the workarounds needed for Ollama embeddings. Then, explore the evolving landscape in the age of AI, including new terms like AEO (Answer Engine Optimization) that are changing how we think about discoverability. Highlights: Neo4j Vector Migration: Understanding the shift from list-based storage to the new vector data type in Neo4j GenAI Procedures Evolution: Navigating multiple versions of GenAI procedures and their current limitations (v2025.11.2) Ollama Workarounds: Using APOC library procedures when bleeding-edge syntax doesn't support your use case Large-Scale Data Loading: Loading 2+ million books from the Goodreads dataset Learning vs. Creating: Finding balance between content consumption and production in a rapidly evolving tech landscape Lenny's Podcast: "The Leadership Skill AI Can't Replace" with Molly Graham Lenny's Podcast: "The Ultimate Guide to AEO: How to Get ChatGPT to Recommend Your Product" with Ethan Smith

Welcome to Breaktime Tech Talks! In this episode, get my latest breakthroughs and insights with Quarkus and Langchain4j, a new vector data type in Neo4j, and details on other projects and events I'm working on. Highlights: MCP Integration Success. Integrating MCP with Quarkus and Langchain4j (Github project). I overcame dependency issues and implemented custom wrapper methods for RAG tools. Advancing Semantic Search. Dive into the new native vector data type in Neo4j, as introduced in a recent developer blog post. One benefit of this new data type for vector search includes data integrity, plus it includes nice migration from the old list format. AI-First Java Book. Hear about my upcoming book, "AI First Java," co-written to help newcomers learn Java with an AI-first approach. I share my perspective on teaching foundational programming concepts in the age of AI-powered tools. Upcoming Events. Preview my speaking engagements for early 2026, including the Glasgow meetup, Jfokus, and Devnexus. Podcast Updates: Hear my thoughts on future guests and feel free to add your thoughts in the BTT feedback form.

In this episode, hear my latest adventures in the world of Java development, focusing on integrating Langchain4j with Quarkus, tackling dependency management, and exploring the evolving landscape of generative AI in production systems. Plus, I highlight upcoming community events and must-watch videos for developers. Highlights: Langchain4j + Quarkus: Read-Only Database Success & Dependency Challenges - progress on a read-only Neo4j database with Langchain4j and Quarkus, caveats around configuration, and the "dependency hell" encountered when adding the MCP server for text-to-Cypher capabilities. Project link: Langchain4j Quarkus Graph RAG app Upcoming Events O'Reilly Graph RAG Fundamentals workshop (virtual, Dec 18) Global Big Data Conference (virtual, Dec 15th) Recommended Videos "Gen AI Grows Up: Building Production Ready Agents on the JVM" by Rod Johnson (GOTO Chicago 2025) Focus: Integrating generative AI into existing Java business solutions, and the new open source project Embabel. "Spring in Autumn with Neo4j" by Gerrit Meier (NODES 2025) Focus: Spring projects and frameworks for integrating with Neo4j, plus tips for other tech stacks.

For the first time ever, Jennifer welcomes a guest to the show! William Lyon gives us a deep dive into the evolving world of AI agents, knowledge graphs, and the concept of memory in artificial intelligence. Episode highlights: William’s career journey: from Neo4j to startups and back again The role of knowledge graphs in agentic memory and reasoning Types of memory in AI agents: episodic, procedural, and more How knowledge graphs can model both user-facing and operational memory The importance of domain-specific data modeling for AI memory systems William’s AI Memory Landscape project: cataloging tools, frameworks, and services in the AI agent memory space Contributions to the project are open, so submit a PR or request! Advice for developers architecting AI agents with memory Other referenced links: GraphStuff.FM podcast AI Memory Landscape project: https://ai-memory-landscape.netlify.app/ Connect with William Lyon: Website: https://lyonwj.com/

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).

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!

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!

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!

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

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!

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!

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

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

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

It's the 50th episode of Breaktime Tech Talks! And to celebrate, I launched a podcast feedback form for you, my listeners. In this 50th episode, follow my latest explorations into Spring AI and GraphRAG. I delve into my attempts to streamline the manual GraphRAG process using Spring AI advisors and tools, sharing the challenges I'm facing, specifically with context parsing from one advisor to the next. I also update the Spring AI starter kit to the 1.0 GA release and recap my Neo4j developer certification livestream. To wrap up, I highlight the Spring AI documentation's AI Concepts page that beautifully blends a blog-post style with key project information.

This week, I simplified my Langchain4j project with improved prompt variable injection. Then hear my perspective on the role of tools vs. agents in AI workflows—looking at how structured processes differ from autonomous systems, especially in the context of Java frameworks and GraphRAG. Get an inside scoop on how I use different AI coding tools: IntelliJ IDEA for in-flow coding, VS Code with agent mode for problem-solving, and ChatGPT for summarizing and refining content. Lastly, hear highlights from an article on building a local RAG app with Quarkus—clear diagrams and step-by-step breakdown of ingestion vs. retrieval workflows.

This week, there were quite a few things I learned: Common steps for implementing GraphRAG in Java using Spring AI and Langchain4j, highlighting key differences in setup and customization. Study prep updates and help on the Neo4j Developer Certification for June! Celebrate Langchain4j’s 1.0 release. Two thought-provoking articles—one on enhancing RAG with graphs, and another analyzing the effectiveness of voice-based interfaces. For a high-level review of steps for GraphRAG in Java, upcoming step-by-step help for prepping to take the Neo4j certification, Langchain4j GA news, and keeping up on tech content, this episode has you covered!

In this episode, I share some hands-on insights from building apps with Langchain4j using Quarkus and Neo4j, and compare it with Spring AI—especially around how each framework handles vector search and GraphRAG workflows. Spoiler: customization in Langchain4j feels a bit clunky. I also dig into one article's critical take on the MCP authorization spec and why its current approach to security is misaligned with how enterprises actually structure identity and access. The article I discuss breaks down both the architectural intentions and the practical enterprise concerns—token handling, overhead, and developer friction. If you’re working at the intersection of GenAI infrastructure and enterprise systems, this one’s for you.