This Week: Knowledge Graph, Neo4j, GraphRAG, Finance

Welcome to This Week in Neo4j, your weekly fix for news from the world of graph databases!
We start this week with a deep dive into Knowledge Graphs and how they work. From there, we have a video interview on how to start with Neo4j, learn how to retrieve info from graphs and embeddings for a chatbot and analyse the Q1 earnings from PepsiCo.

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I hope you enjoy this issue,
Alexander Erdl

COMING UP NEXT WEEK!

Livestream: Neo4j Live: Cognitive Sciences and Dynamic GraphRAG on August 22
Meetup: Meet us in London, UK on August 20
NODES 2024: Register Now! for November 07
All Neo4j Events: Webinars and More
GraphSummit Series: Get Connected With Graphs

FEATURED COMMUNITY MEMBER: José Domínguez

Joé is Co-Founder and VP of Engineering at Blar. He has been in love with AI ever since. Before Blar, he worked at a Startup that helped coach sales teams based on sales call transcriptions and metrics.
Connect with him on LinkedIn.

He presented in a recent livestream “Graph-Powered Code Debugging with GenAI” where we synced repositories, addressed unexpected errors, and demonstrated how you can use a Code Base Agent using graph technology to debug and optimise your code.

KNOWLEDGE GRAPH: What is a Knowledge Graph?

In this article, John Stegeman explains the concept of Knowledge Graphs and how they work. A knowledge graph is an organised representation of real-world entities and their relationships. Entities in a knowledge graph can represent objects, events, situations, or concepts. The relationships between these entities capture the context and meaning of how they are connected.

GRAPH DATABASE: Getting Started with Graph Databases with Jennifer Reif from Neo4j

Chris Engelbert sits down with Jennifer Reif, who explains how Neo4j stores data as entities and relationships. She highlights the advantages of Neo4j over traditional relational databases, especially in handling complex relationships without needing extensive knowledge of the data model upfront.

GRAPHRAG: Hybrid search – Retrieve from Graph and Embeddings

This article by Soumen Mondal explores how to build a powerful chatbot that not only retrieves information from documents but also integrates with knowledge graphs for enhanced insights. He then continues to perform Hybrid search RAG from graph and embeddings. The article also includes a video that shows his entire process.

FINANCIAL ANALYSIS: Update on Pepsi post its Q12024 Earnings

Simon Chen analyses PepsiCo’s Q12024 earnings in a Knowledge Graph for a more insightful view into PepsiCo’s operations and financials than the traditional Company Profile page with siloed tables. From there, you can then launch into the data behind it, e.g. all PepsiCo’s segmented revenues and its largest competitors in each region.