This Week in Neo4j: GraphAcademy, Knowledge Graph, Predictions, GraphRAG and more

Welcome to This Week in Neo4j, your weekly fix for news from the world of graph databases!
In this week’s edition, we look at recent updates to the LLM Fundamentals GraphAcademy Coure, a Knowledge Graph for Nobel Prize Winners, the basics behind GraphRAG and how to predict the French Open.

Did you miss the deadline for the NODES 2024 Call for Papers? As readers of this newsletter, you get a special extension, but you better don’t wait too long!

I hope you enjoy this issue,
Alexander Erdl


Akhil Hemanth is a pragmatic designer with expertise in conceptual design, augmented reality, and construction administration. Committed to democratising emerging technologies, he pushes boundaries in AEC with AI and innovative tech solutions.
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He is already confirmed to speak at NODES 2024. In his session, he demonstrates how Neo4j’s knowledge graph enhances retrieval and generation processes. You will learn about specialised techniques, such as LangSmith evaluators, for maintaining answer consistency and retrieval conditioning for optimal outcomes.

Akhil Hermanth
GRAPHACADEMY: Neo4j and LLM Fundamentals
The course has been updated to reflect the latest Langchain release v0.2. These changes include introducing LCEL (Langchain Expression Language) and using Neo4j as a conversation memory store.
KNOWLEDGE GRAPH: Enhancing Knowledge Graphs with LLMs: A novel approach to keyword extraction and synonym merging
Nobel Prize Outreach (NPO) wants to use Knowledge Graphs to uncover connections between Nobel Prize laureates for storytelling and interactive visualisations, for example, at the Nobel Prize Museum. Valentin Buchner explores how to use GPT-4 to extract and merge keywords from Nobel laureate biographies and lectures, combining them with a subgraph from Wikidata to enhance connectivity and visualisation in Neo4j.
PREDICTIONS: French Open Roland Garros
Have you ever wondered how you can use a knowledge graph to predict the outcome of a tennis tournament? Florent shares his experience analysing data from the French Open Roland Garros.
GRAPHRAG: LLMs -X- GraphDB(Neo4j): Enhancing Retrieval-Augmented Generation (RAG)
Kaarthik Senthil Kumar delves into the synergy between LLMs and Neo4j, uncovering the Retrieval-Augmented Generation (RAG) concept and its specialised form, Graph RAG.


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