Vector indexes allow you to query vector embeddings from large datasets. An embedding is a numerical representation of a data object, such as a text, image, or document. Each word or token in a text is typically represented as high-dimensional vector where each dimension represents a certain aspect of the word’s meaning.
The embedding for a particular data object can be created by both proprietary (such as Vertex AI or OpenAI) and open source (such as sentence-transformers) embedding generators, which can produce vector embeddings with dimensions such as 256, 768, 1536, and 3072. Embeddings can also represent graph structure, not only the semantic content of text, images, or documents. For example, Neo4j Graph Data Science node embedding algorithms can write embeddings that capture graph topology to node properties. Those properties can then be indexed with a vector index and queried with Neo4j vector search as a structural search signal, either on their own or as one source in a hybrid search query.
Vector embeddings are stored as LIST<INTEGER | FLOAT> properties on a node or relationship. As of Neo4j 2025.10, they can also be more efficiently stored as VECTOR properties using Cypher® 25.
For example, the movie The Godfather, has the following plot: "The aging patriarch of an organized crime dynasty transfers control of his clandestine empire to his reluctant son." This is its 1536-dimensional embedding property, where each coordinate in the LIST or VECTOR represents a particular aspect of the plot’s meaning:
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Words that are semantically similar are often represented by vectors that are close to each other in this vector space. This allows for mathematical operations like addition and subtraction to carry semantic meaning. For example, the vector representation of "king" minus "man" plus "woman" should be close to the vector representation of "queen." In other words, vector embeddings are a numerical representation of a particular data object.
A vector index allows you to retrieve a neighborhood of nodes or relationships based on the similarity between the embedding properties of those nodes or relationships and the ones specified in the query.