Zach Blumenfeld

This talk explores how AI models can leverage structured data, specifically within a lakehouse environment, by understanding context in various shapes rather than relying solely on traditional query-based methods. It emphasizes the shift from asking direct questions to enabling AI to infer and utilize information based on its inherent structure and relationships. The core idea is to unlock more sophisticated AI applications by treating data context as a fundamental input.

This talk introduces context graphs as a method to enhance AI agent decision-making beyond simple document retrieval. Context graphs store not only factual information but also past decision traces, precedents, and causal chains. This allows agents to make more informed and explainable decisions by understanding the reasoning behind previous choices, acting with a form of subject matter expertise.

This talk introduces GraphRAG, an architecture that integrates knowledge graphs with AI agents to enhance data retrieval and reasoning. It demonstrates how to build a knowledge graph using Neo4j and leverage it with tools like LangChain and LangGraph for applications such as talent management and skill analysis. The session covers data modeling, querying with Cypher, incorporating vector search for semantic similarity, and building agents that can interact with the graph.

This talk introduces Agentic GraphRAG, a method for simplifying data retrieval across both structured and unstructured sources. It proposes using a knowledge graph as an intermediary layer to enhance agentic workflows. By modeling data within a graph, agents can more accurately decompose complex questions, pull relevant information, and perform analytical tasks that go beyond simple semantic search.