This Week in Neo4j: OpenAI, Deploying on GCP, Neodash, Graph Algorithms, Python, and More

Thanks for tuning in to this week’s newsletter! Check out Gaston Guitart’s article for developers provisioning instances on GCP. This automated deployment system for Neo4j extensions is a workflow consisting of a GCP function and a NodesJS web app. You may also be interested in Eranga Dulshan’s integration of OpenAI semantic search embeddings into a graph database for use with graph data science. Python developers can take a look at the NODES 2022 videos highlighted in Jason Koo’s article “Nodes 2022 for Pythonistas.” Cheers, Yolande Poirier P.S.: If you’re a developer building modern applications with GraphQL, don’t forget to take this short, two-minute survey. We want to hear from you!
Chris Anthes is the founder of, a San Diego-based software startup focused on connecting performing artists and venues with people who enjoy live entertainment. With over 30 years of software development experience under his belt, Chris is never afraid to tackle something completely new. He co-founded his first startup company in 2001, creating one of the world’s first graph databases years before NoSQL databases were cool. You can follow him on LinkedIn. In his NODES 2022 presentation, he covers the process of building a consumer-facing application utilizing GRANDstack, including a comparison of using property graphs vs traditional SQL or document-based databases to model data, handle business logic, and solve unique problems that traditional databases struggle with. Watch it now!
ML: Using OpenAI Semantic Search With Neo4j
In this blog, Eranga Dulshan uses OpenAI semantic search and py2neo package on a graph database of PDF files. He saves the OpenAI generated embedding vectors in Neo4j nodes and calculates the cosine similarity at the database level with graph data science.
VISUALIZATION: Exploring Neodash for 197M Chemical Full-Text Graph
Tom Nijhof, biomedical engineer, explores a chemical graph database with NeoDash. With an input field and a result field, the addition of a full query yields a fuzzy full-text search.
NODES SESSION: Graph Algorithms and Visualization for Clinical Care Support of Pneumonia
Ana Areias and Mengjia Kang take a deep dive into patient journeys through the Medical Information Mart for Intensive Care (MIMIC)-IV de-identified Electronic Medical Records (EMR) data from 2008 – 2019, for patients diagnosed with pneumonia.
PYTHON: NODES 2022 for Pythonistas
With over 40 hours of recorded NODES 2022 videos, it’s nice to have a guide. Jason Koo, Developer Advocate at Neo4j, sifts through it all from the viewpoint of a Python developer.  
GOOGLE CLOUD: Automating Deployment of Neo4j Java Extensions
Gaston Guitart, Neo4j Consulting Engineer, addresses potential challenges to manual deployment of Neo4j extensions including SSH configuration. He proposes a framework for automating deployment on self-managed Google Cloud environments in two git repositories.
INTRODUCTION: Knowledge Graphs
Tiroshan Madushanka introduces knowledge graphs and explores how a knowledge graph about movies represents the actors, directors, producers, and studios involved in each film. He explains the advantages of graph databases to represent complex relationships and connections and the common applications of graph technology.
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    •  Gephi 0.10 is out!Gephi is open-source software for network visualization and analysis. Learn about it.
    • Quarkus Neo4j, a Quarkus extension to connect to the Neo4j graph database. It enables the use of the Neo4j Java Driver in both JVM mode and native executables. It provides configuration properties to configure all relevant aspects of the driver. Check it out.
    • Built-in JDBC connector alpha. Major rework of the JDBC connectivity layer of the Neo4j extension for Liquibase is currently available for testing.
    •  Graph Data Structure with Java. Rafael del Nero explains how to create basic graph data structures in Java. Discover his tutorials.
    •  Depth-First Search with Java. In this post, Rafael del Nero explains depth-first search traversal algorithms such as preorder, postorder, and in-order.