Building graph vector search using VectorETL
We launched VectorETL just over 2 months ago and every few days, we stumble on new and interesting ways to use VectorETL to make vector data processing faster. As part of use cases that we explore, we are always looking for opportunities where we can easily integrate VectorETL into an already existing AI focused workflow (RAG, Enterprise Search etc.)
One of the more interesting vector target integrations we have is Neo4j, which is one of the most widely used graph databases in the world. Using graphs (and by extension Neo4j) for Generative AI use cases has been a hot topic in the last few years due to its superior search and retrieval capabilities.
In this article, we’ll explore how to use VectorETL to quickly and easily build a powerful graph search application using Neo4j. We’ll cover the entire process from data ingestion to querying the graph using natural language, leveraging the power of vector embeddings and large language models.
Neo4j provides most users with a free account where you can create an entry level graph. You can just go to the Neo4j site here and create an account, create a database and get your connection details.
