Lucene

AI on Your Lakehouse: Context Comes in Shapes, Not Queries — Zach Blumenfeld, Neo4j

Jul 23, 2026 · 1:59:10

Zach Blumenfeld of Neo4j argues that AI agents need context in shapes rather than queries, building three reusable graph shapes on lakehouse data to solve agent hallucinations and missed connections. The shapes include a connection semantic layer on top of BigQuery (or Databricks/Snowflake) that helps agents navigate join paths across hundreds of tables, a deterministic table-of-contents tree that lets agents traverse document folders and links without vector search, and a Leiden community-detection theme shape that surfaces unknown patterns and documentation gaps. Blumenfeld demonstrates with an auto-repair chain scenario, showing how these shapes enable an agent to answer specific repair questions and estate-level questions like what documentation is missing or what failure patterns exist, by treating context as navigable structure rather than a single query.

When Vectors Break Down: Graph-Based RAG for Dense Enterprise Knowledge - Sam Julien, Writer

Jul 22, 2025 · 15:47

Sam Julien, Director of Developer Relations at Writer, explains how the company built a graph-based RAG system that achieved 86.31% accuracy on the RobustQA benchmark and sub-second response times, significantly outperforming vector search approaches for dense enterprise knowledge. The team moved from simple vector retrieval to a graph-based approach to handle concentrated data where similar terms appear frequently, solving problems like inaccurate chunking and failure with similar documents. They built a specialized model to convert data into graph structures, stored as JSON in a Lucene-based search engine, and incorporated Fusion-in-Decoder with knowledge graphs to lower hallucination rates. Key decisions included focusing on customer needs over hype, staying flexible based on team expertise, and letting research challenge assumptions, leading to features like multi-hop reasoning and complex data format handling.