Vector indexes

Node vector indexes were released as a public beta in Neo4j 5.11 and general availability in Neo4j 5.13.

Vector indexes enable similarity searches and complex analytical queries by representing nodes or properties as vectors in a multidimensional space.

The following resources provide hands-on tutorials for working with LLMs and vector indexes in Neo4j:

Neo4j vector indexes are powered by the Apache Lucene indexing and search library.^[1]

Example graph

The examples on this page use the Neo4j movie recommendations dataset, focusing on the plot and embedding properties of Movie nodes. The embedding property consists of a 1536-dimension vector embedding of the plot and title property combined.

The graph contains 28863 nodes and 332522 relationships.

To recreate the graph, download and import this dump file to an empty Neo4j database (running version 5.13 or later). Dump files can be imported for both Aura and on-prem instances.

The dump file used to load the dataset contains embeddings generated by OpenAI, using the model text-embedding-ada-002. The examples on this page do not show how to generate embeddings, or how to use the procedures db.create.setNodeVectorProperty and db.create.setRelationshipVectorProperty to set vector properties. More information can be found in the GenAI documentation - Embeddings & Vector indexes tutorial.

Vectors and embeddings in Neo4j

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. In Neo4j, vector embeddings are stored as LIST<INTEGER | FLOAT> properties on a node or relationship.

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 element in the LIST 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.

Create vector indexes

A vector index is a single-label, single-property index for nodes or a single-relationship-type, single-property index for relationships. It can be used to index nodes or relationships by LIST<INTEGER | FLOAT> properties valid to the dimensions and vector similarity function of the index. Note that the available vector index providers (vector-2.0 (default) and vector-1.0) support different index schemas, property value types, and vector dimensions. For more information, see Vector index providers for compatibility.

A vector index is created by using the CREATE VECTOR INDEX command. It is recommended to give the index a name when it is created. If no name is given when created, a random name will be assigned. As of Neo4j 5.16, the index name can also be given as a parameter: CREATE VECTOR INDEX $name ….

The index name must be unique among both indexes and constraints.
A newly created index is not immediately available but is created in the background.

Creating indexes requires the CREATE INDEX privilege.

Create vector index for Movie nodes on the embedding property Introduced in 5.15

CREATE VECTOR INDEX moviePlots IF NOT EXISTS (1)
FOR (m:Movie)
ON m.embedding
OPTIONS {indexConfig: {
 `vector.dimensions`: 1536,
 `vector.similarity_function`: 'cosine'
}} (2)

1 The CREATE VECTOR INDEX command is optionally idempotent. This means that its default behavior is to throw an error if an attempt is made to create the same index twice. With IF NOT EXISTS, no error is thrown and nothing happens should an index with the same name, schema or both already exist. It may still throw an error should a constraint with the same name exist. As of Neo4j 5.17, an informational notification is returned when nothing happens, showing the existing index which blocks the creation.

2 The OPTIONS map is mandatory since a vector index cannot be created without setting the vector dimensions and similarity function. In this example, the vector dimension is set to 1536 and the vector similarity function is cosine, which is generally the preferred similarity function for text embeddings. To read more about the available similarity functions, see Euclidean and cosine similarity functions.

Prior to Neo4j 5.15, node vector indexes were created using the db.index.vector.createNodeIndex procedure.

You can also create a vector index for relationships with a particular type on a given property using the following syntax:

Create a vector index for a relationship type on a single property Introduced in 5.18

CREATE VECTOR INDEX name IF NOT EXISTS
FOR ()-[r:REL_TYPE]-() ON (r.embedding)
OPTIONS {indexConfig: {
 `vector.dimensions`: $dimension,
 `vector.similarity_function`: $similarityFunction
}}

Query vector indexes

To query a node vector index, use the db.index.vector.queryNodes procedure.

Signature for db.index.vector.queryNodes

db.index.vector.queryNodes(indexName :: STRING, numberOfNearestNeighbours :: INTEGER, query :: ANY) :: (node :: NODE, score :: FLOAT)

The indexName refers to the unique name of the vector index to query.
The numberOfNearestNeighbours refers to the number of nearest neighbors to return.
The query vector refers to the LIST<INTEGER | FLOAT> in which to search for the neighborhood.

The procedure returns the neighborhood of nodes with their respective similarity scores, ordered by those scores. The scores are bounded between 0 and 1, where the closer to 1 the score is, the more similar the indexed vector is to the query vector.

Find the 5 movies with the most similar plot to The Godfather

MATCH (m:Movie {title: 'Godfather, The'})
CALL db.index.vector.queryNodes('moviePlots', 5, m.embedding)
YIELD node AS movie, score
RETURN movie.title AS title, movie.plot AS plot, score

Result

+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| title                      | plot                                                                                                                                                                                                                     | score              |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "Godfather, The"           | "The aging patriarch of an organized crime dynasty transfers control of his clandestine empire to his reluctant son."                                                                                                    | 1.0                |
| "Godfather: Part III, The" | "In the midst of trying to legitimize his business dealings in New York and Italy in 1979, aging Mafia don Michael Corleone seeks to avow for his sins while taking a young protégé under his wing."                     | 0.9648237228393555 |
| "Godfather: Part II, The"  | "The early life and career of Vito Corleone in 1920s New York is portrayed while his son, Michael, expands and tightens his grip on his crime syndicate stretching from Lake Tahoe, Nevada to pre-revolution 1958 Cuba." | 0.9547788500785828 |
| "Scarface"                 | "An ambitious and near insanely violent gangster climbs the ladder of success in the mob, but his weaknesses prove to be his downfall."                                                                                  | 0.9367183446884155 |
| "Jane Austen's Mafia!"     | "Takeoff on the Godfather with the son of a mafia king taking over for his dying father"                                                                                                                                 | 0.9366795420646667 |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

Note that all movies returned have a plot centred around criminal family organizations. The score results are returned in descending order, where the best matching result entry is put first (in this case, The Godfather has a similarity score of 1.0, which is to be expected as the index was queried with this specific property). If the query vector itself is not wanted, adding the predicate WHERE score < 1 removes identical vectors.

To query a relationship vector index, use the db.index.vector.queryRelationships procedure.

Signature for db.index.vector.queryRelationships Introduced in 5.18

db.index.vector.queryRelationships(indexName :: STRING, numberOfNearestNeighbours :: INTEGER, query :: ANY) :: (relationship :: RELATIONSHIP, score :: FLOAT)

db.index.vector.queryRelationships has the same argument descriptions as db.index.vector.queryNodes.

Use Vector functions to compute the similarity score between two specific vector pairs without using a vector index.

Performance suggestions

Vector indexes can take advantage of the incubated Java 20 Vector API for noticeable speed improvements. If you are using a compatible version of Java, you can add the following setting to your configuration settings:

Configuration settings

server.jvm.additional=--add-modules jdk.incubator.vector

Show vector indexes

To list all vector indexes in a database, use the SHOW VECTOR INDEXES command. This is the same SHOW command as for other indexes, with the index type filtering on VECTOR.

Listing indexes requires the SHOW INDEX privilege.

Example 1. Show all vector indexes

Show vector indexes with the default return columns

SHOW VECTOR INDEXES

Result

+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| id | name         | state    | populationPercent | type     | entityType | labelsOrTypes | properties        | indexProvider | owningConstraint | lastRead                 | readCount |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 2  | "moviePlots" | "ONLINE" | 100.0             | "VECTOR" | "NODE"     | ["Movie"]     | ["embedding"]     | "vector-2.0"  | NULL             | 2024-05-07T09:19:09.225Z | 47        |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

For a full description of all return columns, see Search-performance indexes → Result columns for listing indexes.

Example 2. Show vector indexes with full or filtered details

To return full vector index details, use YIELD *.

Show all vector indexes with all return columns

SHOW VECTOR INDEXES YIELD *

Result

+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| id | name         | state    | populationPercent | type     | entityType | labelsOrTypes | properties        | indexProvider | owningConstraint | lastRead                 | readCount | trackedSince             | options                                                                                                         | failureMessage | createStatement                                                                                                                                                                                  |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| 2  | "moviePlots" | "ONLINE" | 100.0             | "VECTOR" | "NODE"     | ["Movie"]     | ["embedding"]     | "vector-2.0"  | NULL             | 2024-05-07T09:19:09.225Z | 47        | 2024-05-07T08:26:19.072Z | {indexConfig: {`vector.dimensions`: 1536, `vector.similarity_function`: "COSINE"}, indexProvider: "vector-2.0"} | ""             | "CREATE VECTOR INDEX `moviePlots` FOR (n:`Movie`) ON (n.`embedding`) OPTIONS {indexConfig: {`vector.dimensions`: 1536,`vector.similarity_function`: 'COSINE'}, indexProvider: 'vector-2.0'}"     |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

To return only specific details, specify the desired column name(s) after the YIELD clause.

Show all vector indexes with filtered return columns

SHOW VECTOR INDEXES YIELD name, type, entityType, labelsOrTypes, properties

Result

+----------------------------------------------------------------------+
| name         | type     | entityType | labelsOrTypes | properties    |
+----------------------------------------------------------------------+
| "moviePlots" | "VECTOR" | "NODE"     | ["Movie"]     | ["embedding"] |
+----------------------------------------------------------------------+

Drop vector indexes

A vector index is dropped by using the same command as for other indexes, DROP INDEX. As of Neo4j 5.16, the index name can also be given as a parameter when dropping an index: DROP INDEX $name.

Dropping indexes requires the DROP INDEX privilege.

Drop a vector index

DROP INDEX moviePlots

Vector index providers for compatibility

As of Neo4j 5.18, the default and preferred vector index provider is vector-2.0. Previously created vector-1.0 indexes will continue to function. New indexes can still be created with the vector-1.0 provider if it is specified in the OPTIONS map.

Learn more about vector index provider differences

Supported vector-1.0 vector-2.0

Supported	`vector-1.0`	`vector-2.0`
Index schema	Single-label, single-property index for nodes. No relationship support.	Single-label, single-property index for nodes. Single-type, single-property index for relationships.
Indexed property value type	`LIST<FLOAT>`	`LIST<INTEGER \| FLOAT>`
Indexed vector dimension	`INTEGER` between `1` and `2048` inclusive.	`INTEGER` between `1` and `4096` inclusive.
Cosine similarity vector validity	All vector components can be represented finitely in IEEE 754 *single* precision. Its -norm is non-zero and can be represented finitely in IEEE 754 *single* precision.	All vector components can be represented finitely in IEEE 754 *double* precision. Its -norm is non-zero and can be represented finitely in IEEE 754 *double* precision. The ratio of each vector component with its -norm can be represented finitely in IEEE 754 *single* precision.

Index schema

Single-label, single-property index for nodes.

No relationship support.

Single-label, single-property index for nodes.

Single-type, single-property index for relationships.

Indexed property value type

LIST<FLOAT>

LIST<INTEGER | FLOAT>

Indexed vector dimension

INTEGER between 1 and 2048 inclusive.

INTEGER between 1 and 4096 inclusive.

Cosine similarity vector validity

All vector components can be represented finitely in IEEE 754 single precision.

Its -norm is non-zero and can be represented finitely in IEEE 754 single precision.

All vector components can be represented finitely in IEEE 754 double precision.

Its -norm is non-zero and can be represented finitely in IEEE 754 double precision.

The ratio of each vector component with its -norm can be represented finitely in IEEE 754 single precision.

Euclidean and cosine similarity functions

The choice of similarity function affects which indexed vectors are considered similar, and which are valid. The semantic meaning of the vector may itself dictate which similarity function to choose. Refer to the documentation for the particular vector embedding model you are using, as it may suggest a preference for certain similarity functions. Otherwise, being able to differentiate between the various similarity functions can assist in making a more informed decision.

Table 1. Similarity functions
Name	Case insensitive argument	Key similarity feature
Euclidean	`"euclidean"`	distance
Cosine	`"cosine"`	angle

For -normalized vectors (unit vectors), Euclidean and cosine similarity functions produce the same similarity ordering.

Learn more about the Euclidean similarity function

Euclidean similarity is useful when the distance between the vectors is what determines how similar two vectors are.

A valid vector for a Euclidean vector index is when all vector components can be represented finitely in IEEE 754 single precision.^[2]

Euclidean interprets the vectors in Cartesian coordinates. The measure is related to the Euclidean distance, i.e., how far two points are from one another. However, that distance is unbounded and less useful as a similarity score. Euclidean similarity bounds the square of the Euclidean distance.

The Euclidean of vector v and vector u is defined as 1 over the quantity 1 plus the square of the l2-norm of vector v subtract vector u, which exists in the bounded set of real numbers between 0 exclusive and 1 inclusive.

Learn more about the cosine similarity function

Cosine similarity is used when the angle between the vectors is what determines how similar two vectors are.

A valid vector for a cosine vector index is when:

All vector components can be represented finitely in IEEE 754 double precision.
Its -norm is non-zero and can be represented finitely in IEEE 754 double precision.
The ratio of each vector component with its -norm can be represented finitely in IEEE 754 single precision.

Cosine similarity interprets the vectors in Cartesian coordinates. The measure is related to the angle between the two vectors. However, an angle can be described in many units, sign conventions, and periods. The trigonometric cosine of this angle is both agnostic to the aforementioned angle conventions and bounded. Cosine similarity rebounds the trigonometric cosine.

The cosine of vector v and vector u is defined as half of the quanity 1 plus the scalar product of v hat u hat, which equals half of the quantity 1 plus the scalar product of vector v vector u over the product of the l2-norm of vector v and the l2 norm ov vector u, which exists in the bounded set of real numbers between 0 inclusive and 1 inclusive.

In the above equation the trigonometric cosine is given by the scalar product of the two unit vectors.

Vector index procedures

Usage Procedure Description

Usage	Procedure	Description
Create node vector index.	`db.index.vector.createNodeIndex`	Create a vector index for the specified label and property with the given vector dimension using the given similarity function. Replaced by the `CREATE VECTOR INDEX` command.
Use node vector index.	`db.index.vector.queryNodes`	Query the given node vector index. Returns the requested number of approximate nearest neighbor nodes and their similarity score, ordered by score.
Use relationship vector index.	`db.index.vector.queryRelationships`	Query the given relationship vector index. Returns the requested number of approximate nearest neighbor relationships and their similarity score, ordered by score. Introduced in 5.18
Set node vector property.	`db.create.setNodeVectorProperty`	Update a given node property with the given vector in a more space-efficient way than directly using `SET`. Replaces `db.create.setVectorProperty`. Beta Introduced in 5.13
Set node vector property.	`db.create.setVectorProperty`	Replaced by `db.create.setNodeVectorProperty`. Deprecated Beta
Set relationship vector property.	`db.create.setRelationshipVectorProperty`	Update a given relationship property with the given vector in a more space-efficient way than directly using `SET`. Beta Introduced in 5.18

Create node vector index.

db.index.vector.createNodeIndex

Create a vector index for the specified label and property with the given vector dimension using the given similarity function. Replaced by the CREATE VECTOR INDEX command.

Use node vector index.

db.index.vector.queryNodes

Query the given node vector index. Returns the requested number of approximate nearest neighbor nodes and their similarity score, ordered by score.

Use relationship vector index.

db.index.vector.queryRelationships

Query the given relationship vector index. Returns the requested number of approximate nearest neighbor relationships and their similarity score, ordered by score. Introduced in 5.18

Set node vector property.

db.create.setNodeVectorProperty

Update a given node property with the given vector in a more space-efficient way than directly using SET. Replaces db.create.setVectorProperty. Beta Introduced in 5.13

Set node vector property.

db.create.setVectorProperty

Replaced by db.create.setNodeVectorProperty. Deprecated Beta

Set relationship vector property.

db.create.setRelationshipVectorProperty

Update a given relationship property with the given vector in a more space-efficient way than directly using SET. Beta Introduced in 5.18

Limitiations and known issues

As of Neo4j 5.13, the vector index is no longer a beta feature. It does, however, still contain some limitations and known issues.

Limitations

The query is an approximate nearest neighbor search. The requested k nearest neighbors may not be the exact k nearest, but close within the same wider neighborhood.
For large requested nearest neighbors, k, close to the total number of indexed vectors, the search may retrieve fewer than k results.
Only one vector index can be over a schema. For example, you cannot have one Euclidean and one cosine vector index on the same label-property key pair.
No provided settings or options for tuning the index.
Changes made within the same transaction are not visible to the index.

Known issues

The following table lists the known issues and, if fixed, the version in which they were fixed:

Known issues Fixed in

Procedure signatures from SHOW PROCEDURES will render the vector arguments with a type of ANY rather than the semantically correct type of LIST<INTEGER | FLOAT>.

The types are still enforced as LIST<INTEGER | FLOAT>.

Only node vector indexes are supported.

Neo4j 5.18

Vector indexes cannot be assigned autogenerated names.

Neo4j 5.15

There is no Cypher syntax for creating a vector index.

Use the procedure db.index.vector.createNodeIndex to create the a vector index. Procedure signature:

db.index.vector.createNodeIndex(indexName :: STRING, label :: STRING, propertyKey :: STRING, vectorDimension :: INTEGER, vectorSimilarityFunction :: STRING)

Neo4j 5.15

The standard index type filtering for SHOW INDEXES command is missing.

Filtering on vector indexes can be done with the WHERE clause as well:

SHOW INDEXES
WHERE type = 'VECTOR'

Neo4j 5.15

Vector indexes may incorrectly reject valid queries in a cluster setting. This is caused by an issue in the handling of index capabilities on followers.

Because index capabilities will be correctly configured on a restart, this issue can be worked around by rolling the cluster after vector index creation.

For more information about clustering in Neo4j, see the Operations Manual → Clustering.

Neo4j 5.14

Querying for a single approximate nearest neighbor from an index would fail a validation check. Passing a null value would also provide an unhelpful exception.

Neo4j 5.13

Vector index queries throw an exception if the transaction state contains changes. This means that writes may only take place after the last vector index query in a transaction.

To work around this issue if you need to run multiple vector index queries and make changes based on the results, you can run the queries in a CALL { ... } IN TRANSACTIONS clause to isolate them from the outer transaction’s state.

Neo4j 5.13

SHOW PROCEDURES does not show the vector index procedures:

The procedures are still usable, just not visible.

Neo4j 5.12

Passing null as an argument to some of the procedure parameters can generate a confusing exception.

Neo4j 5.12

The creation of the vector index skipped the check to limit the dimension to 2048.

Vector indexes configured with a dimension greater than 2048 in Neo4j 5.11 should continue to work after the limitation is applied.

Neo4j 5.12

The validation for cosine similarity verifies that the vector’s -norm can be represented finitely in IEEE 754 double precision, rather than in single precision. This can lead to certain large component vectors being incorrectly indexed, and return a similarity score of ±0.0.

Neo4j 5.12

db.index.vector.queryNodes query vector validation is incorrect with a cosine vector index. The -norm validation only considers the last component of the vector. If that component is ±0.0, an otherwise valid query vector will be thrown as invalid. This can also result in some invalid vectors being used to query, and return a similarity score of ±0.0.

For -normalized vectors (unit vectors), thus having unit length The l2-norm of vector v equals 1 , Euclidean and cosine similarity functions produce the same similarity ordering. It is recommended to normalize your vectors (if needed), and use a Euclidean vector index.

Neo4j 5.12

The vector index createStatement field from SHOW INDEXES does not correctly escape single quotes in index names, labels, and property keys.

Neo4j 5.12

Copying a database store with a vector index does not log the recreation command, and instead logs an error:

ERROR: [StoreCopy] Unable to format statement for index 'index-name'

Due to an:

java.lang.IllegalArgumentException: Did not recognize index type VECTOR

If a store copy is required, make a note of the information in the createStatement column returned from the SHOW INDEX command. For example:

SHOW INDEXES YIELD type, createStatement
WHERE type = 'VECTOR'
RETURN createStatement

Neo4j 5.12

Some of the protections preventing the use of new features during a database rolling upgrade are missing. This can result in a transaction to create a vector index on a cluster member running Neo4j 5.11 and distributing it to other cluster members running an older Neo4j version. The older Neo4j versions will fail to understand the transaction.

Ensure that all cluster members have been updated to use Neo4j 5.11 (or a newer version) before calling dbms.upgrade() on the system database. Once committed, vector indexes can be safely created on the cluster.

Neo4j 5.12

1. Lucene implements a Hierarchical Navigable Small World (HNSW) Graph to perform a k approximate nearest neighbors (k-ANN) query over the vector fields. For more information, see Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs — Yury A. Malkov and Dmitry A. Yashunin

2. IEEE Standard for Floating-Point Arithmetic