Breaktime Tech Talks

Breaktime Tech Talks

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Breaktime Tech Talks episodes

  • Ep71: GraphRAG Learnings + Langchain4j Apps for Production

    This week, I share hard-won lessons from building a GraphRAG application with Neo4j in Python, plus standout tips from Lize Raes's Devoxx Belgium talk on taking Langchain4j apps to production.

    GraphRAG with Neo4j

    • Built a Python GraphRAG app using the Neo4j GraphRAG package — knowledge graph construction, retrievers (vector, graph, text-to-cypher), and agentic orchestration
    • Key lesson: don't let the LLM decide your entire data model. Providing node types, relationship types, and patterns as boundaries dramatically improves results
    • Expect iteration — retrieval testing will send you back to refine your KG construction
    • Github code: Neo4j GraphRAG Python package

      • Langchain4j for Production (Lize Raes, Devoxx Belgium)

      • Wrap RAG as an agent tool for multi-call retrieval instead of single-shot pipelines
      • Filter available tools programmatically by domain to keep agents focused
      • Wire sub-agents as @Tool for clean multi-agent orchestration
      • Use immediate responses to skip the LLM summarization hop — saves tokens and latency
      • 13-step walkthrough for production-grade agentic systems
      • YouTube link: Level Up Your Langchain4j Apps for Production (Lize Raes, Devoxx Belgium 2025)
        • 15 min
      • Ep70: Devnexus Conference Recap + No-LLM Agentic Workflow

        Hear my recent experience at the Devnexus conference in Atlanta, where I delivered two sessions and connected with so many amazing people!

        • Devnexus session 1: "Agents, Tools, and MCP, Oh My! Next Level AI Concepts for Developers" — a redesigned solo talk breaking down AI building blocks (agents, tool calls, context management, memory, and MCP) so developers can mix and match components for their own stack.
          • Key takeaway: AI systems are much more than just the LLM — developers play a critical role in designing the surrounding architecture.
          • Devnexus session 2: "Supercharging Applications with Java, Graphs, and a Touch of AI" (code repo 1, code repo 2) — a joint session with Erin Schnabel building an LLM-powered role-playing game using Langchain4j, Quarkus, and Neo4j.
            • Multiple approaches: plain LLM chat, prompt engineering, and RAG with Neo4j as the vector/graph store, chunking documents while preserving structure via graph relationships.
            • Our "Three Cs" challenge: Continuity (maintaining storyline), Context (growing context window), and Creativity (keeping the LLM on track without going off the rails).
            • Splitting responsibilities between the LLM and a deterministic engine significantly improved results — a pattern developers should consider for complex AI apps.
            • App redesign with an agentic architecture: dice roll, narration, suggestion, checkpoint, and recap agents — with the last three running concurrently for better performance.
            • Markdown file (in one app) for agentic memory, enabling easy edits, rollbacks, and incremental indexing during live gameplay.
            • Content spotlight: "No Keys, No LLM — Building a Wikidata Definition API with Embabel" — an article showcasing an agentic Java application that uses zero LLM. Embabel (a Java agentic framework) handles planning and execution with structured inputs/outputs, no external or local model required.
              • Could the no-LLM agent pattern see broader adoption, or is it a niche experiment?
              • New episodes will now use platform-agnostic Podfollow links.
              • New blog post on jmhreif.com about Cypher AI procedures.
                • 21 min
              • Ep69: Growth through Challenge + Rise of Agentic AI

                Jennifer shares highlights from a week full of spontaneity and preparation.

                Highlights:

                • The Bootiful Podcast (Coffee + Software) with Josh Long
                • Impromptu livestream with Josh on building a Spring + Neo4j application with just 10 minutes prep
                • Participated in an X Space panel on the rise of agentic AI with experts from AWS, Nvidia, and Brokk
                • Final preparations for two Devnexus sessions and other activities
                • The reality of setting boundaries as a developer advocate
                • Content highlights: Brock AI-native coding platform and DICE knowledge graph library for Java

                  • Key Themes: Growth through unexpected challenges, maintaining quality over quantity, and leaning into spontaneous opportunities

                  Links:

                  • The Bootiful Podcast episode
                  • Coffee + Software livestream with Josh recording
                  • X Space recording on The Rise of Agentic AI
                  • Brokk YouTube video
                  • Embabel DICE GitHub project

                    • Next Week: Devnexus in Atlanta! Visit the Neo4j booth.

                    12 min
                  • Ep68: Career Highlights + OGM Alternatives

                    In this episode, hear my reflections on eight years as a Developer Advocate at Neo4j - learning in public, teaching before feeling “ready”, and navigating the constant balance between deep technical work and community engagement. Get updates on what I'm currently focused on: upcoming events, writing a more complex chapter of the Java book, sharpening Cypher skills, and exploring an article that challenges the default use of Object Graph Mappers (OGMs) in graph applications.

                    Highlights

                    • 8 Years in Advocacy

                      • Learning fast by presenting and teaching

                      • Balancing deep work, travel, and ad hoc collaboration

                      • Adapting to the accelerating pace of tech and AI

                      • Current Projects

                        • Preparing for Devnexus and upcoming virtual events

                        • Contributing to Road to NODES AI workshops

                        • Writing a more advanced Java book chapter (avoiding the editing loop)

                        • Intentionally improving Cypher skills through deeper practice

                        • Rethinking OGMs

                          • Exploring the article “The Very Slowly Ticking Time Bomb, Your Graph Persistent Stack”

                          • Questioning whether OGMs add unnecessary translation layers in graph apps

                          • Considering alternatives

                          • Expanding the toolbox — no one-size-fits-all solution

                            Events

                            • Devnexus (Atlanta, GA)

                            • San Francisco Bay ACM (Virtual Event)

                            • Road to NODES AI Workshops (Free, virtual)

                              16 min
                            • Ep67: Conference Recap + Cypher Query Patterns

                              Fresh back from Jfokus in Stockholm! This week, I'm sharing highlights from the conference and diving into advanced Cypher techniques that make graph databases shine.

                              Highlights:

                              • Jfokus 2025 recap: Viking themes, inspiring community, and lots of content
                              • Book writing updates and upcoming March events
                                • Combination of outlining and writing in my process
                                • Joint session prep is stretching my application development skills
                                • Content: 10 things that are easier in Cypher than in SQL
                                  • Why aggregation without GROUP BY changed everything for me
                                  • Pattern comprehension, map projections, and where to level up
                                  • Key takeaway: Graph databases excel at path patterns and relationships.

                                    • Resources mentioned:

                                    • "10 Things You Can Do With Cypher That Are Hard With SQL" by Michael Hunger
                                      • 12 min
                                    • Ep66: Neo4j Data Loading at Scale + Vector Search Filtering

                                      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!

                                            15 min
                                          • Ep65: RAG Filtering + Context Graphs with Neo4j

                                            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!

                                              12 min
                                            • Ep64: Neo4j Vector Migration + Learning in the AI Era

                                              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
                                                • 11 min
                                              • Ep63: MCP Integration Success + Advancing Semantic Search

                                                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.

                                                  11 min
                                                • Ep62: Quarkus Langchain4j Updates + Production-Ready Agents on JVM

                                                  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.
                                                              • 10 min

                                                            About Breaktime Tech Talks

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                                                            A bite-sized tech podcast for busy developers where we’ll briefly cover technical topics, new snippets, and more in short time blocks. Your host, Jennifer Reif, is an avid developer and problem-solver…