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!
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For Graph Database Beginners, I picked the Cypher Aggregations course this week. This one is a bit more advanced, but if you followed this segment for a while, I am sure you can do it!
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I hope you enjoy this issue,
Alexander Erdl
COMING UP NEXT WEEK!
- Livestream: Neo4j Live: ICIJ Datashare – Turning Documents into Knowledge on June 25
- Conferences: Find us at AI Engineer World Fair, San Francisco on June 25, KCDC, Kansas City on June 27
- Meetup: Meet us in Mumbai, IN on June 22, Singapore, SG on June 26, Melbourne, AU on June 27
- All Neo4j Events: Webinars and More
- GraphSummit Series: Get Connected With Graphs
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GETTING STARTED WITH GRAPHS
- GRAPHACADEMY: Cypher Aggregations
- WATCH: Introduction to Neo4j
- TRY: Neo4j AuraDB Free
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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.
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.
POST OF THE WEEK: Sam Julien
What’s graph-based RAG (retrieval-augmented generation) and why should you care? pic.twitter.com/QTLQQ0vncT
— Sam Julien (@samjulien) June 14, 2024
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