Neo4j has taken off as a transactional graph database management platform, with the introduction of Cypher query language allowing expressive and efficient querying. The platform supports graph analytics, including state-of-the-art algorithms and custom optimizations for its graph database model. Neo4j is now venturing into machine learning (ML) and deep learning, aiming to automate both steps of the process: learning about entities and the rules governing their behaviors and interactions with each other. Graph embeddings transform nodes into vectors, preserving topology and connectivity, and can be used as features for classification or clustering. The dominant school of thought in artificial intelligence is deep learning, which automates learning representations through statistical models and learns parameters from past data. Various options are available to implement graph embeddings in Neo4j, including Java-based implementations like Deeplearning4j, a hybrid approach using Jython and JyNI, Py4J for object sharing between Python and Java contexts, GraalVM's universal virtual machine, and Python drivers that read data from a running Neo4j instance. Benchmarking experiments were conducted on two graph datasets, BlogCatalog and Flickr, showing varying performance across different approaches.