Graph Databases

Jul 23, 2026 · 1:59:10

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

The presentation introduces a methodology for enhancing AI agent capabilities when querying large lakehouse environments (BigQuery, Databricks, Snowflake). The core argument is that standard vector search or Text2SQL methods often fail due to insufficient global context. The solution involves building three reusable 'graph shapes'—Trees, Communities, and Connections—on top of the data metadata using Neo4j. These shapes create a semantic layer that allows agents to understand complex relationships, patterns, and document hierarchies, enabling them to answer high-level, estate-wide questions.

Key takeaways

1. Context is in Shapes, Not Queries

   AI agents need more than just data access; they require a structured understanding of how data elements relate. Graph shapes provide this necessary context to prevent the agent from being 'confidently wrong' when dealing with massive datasets.

2. The Three Core Shapes 10:25

   1) **Trees (Table of Contents):** Provides a containment tree structure for unstructured data, allowing agents to navigate document hierarchies and sections. 2) **Communities (Themes):** Surfaces global, unknown patterns by clustering highly interconnected nodes using algorithms like Leiden. 3) **Connections (Semantic Layer):** Builds a metadata graph over structured warehouse schemas, guiding the agent on how tables join without requiring full ETL.

3. Agentic Workflow and Tooling 25:00

   The process relies heavily on agents (e.g., Claude Code) utilizing specialized tools like the NeoForj CLY, which allows the agent to write custom Cypher queries based on the provided graph schema/specs.

• AI Engineer
• Graph Databases
• Semantic Layering
• AI Agents (LLMs)
• Data Lakehouse Architecture
• Knowledge Graph Modeling
• Cypher Query Language
• Graph Academy
• NeoForj Graph Intelligence Platform

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Jul 23, 2026 · 20:54

Citation Needed: Provenance for LLM-Built Knowledge Graphs — Daniel Chalef, Zep AI

The synthesis of facts by Large Language Models (LLMs) often destroys the original source attribution or 'paper trail.' Daniel Chalef argues that traditional methods like simple Source IDs fail when data undergoes complex transformations, such as entity merging or fact invalidation. He proposes that provenance for LLM-built knowledge graphs must itself be a graph structure. The open-source framework Graphiti models sources as 'episodes' and uses graph walks to trace every derived fact back to its original source(s), ensuring compliance (e.g., GDPR deletion) and verifiable veracity.

Key takeaways

1. LLM Synthesis Destroys Provenance

   Because LLMs synthesize facts non-deterministically, the resulting output artifact may not appear verbatim in the source inputs, making simple source ID tracking insufficient for complex agent applications.

2. Provenance Requires a Graph Structure 7:00

   To maintain lineage through mutations and merges, provenance must be modeled as a knowledge graph where facts are derived from linked sources (episodes). Tracing a fact's origin then becomes a simple 'graph walk.'

3. Graphiti Enables Robust Lineage Management 10:06

   The Graphiti framework models source data as episodes, allowing derived facts to inherit all necessary links. This structure supports complex operations like metadata projection (e.g., filtering only 'EHR' verified sources) and GDPR-compliant deletion.

• AI Engineer
• Provenance
• Knowledge Graphs
• LLMs
• Graph Databases
• GDPR Compliance
• Data Lineage
• Graphiti
• Zep

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