Vector indexes

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. 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.

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 information about how embeddings can be generated and stored as properties, see:

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 (deprecated)) 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. 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

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. An informational notification is returned when nothing happens, showing the existing index which blocks the creation.

2 To read more about the available configuration settings, see Configuration settings. In this example, the vector dimension is explicitly set to 1536 and the vector similarity function to 'cosine', which is generally the preferred similarity function for text embeddings. To read more about the available similarity functions, see Cosine and Euclidean similarity functions.

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

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

Configuration settings

For more information about the values accepted by different index providers, see Vector index providers for compatibility.

`vector.dimensions`

The dimensions of the vectors to be indexed. For more information, see Vectors and embeddings in Neo4j. This setting can be omitted, and any LIST<INTEGER | FLOAT> can be indexed and queried, separated by their dimensions, though only vectors of the same dimension can be compared. Setting this value adds additional checks that ensure only vectors with the configured dimensions are indexed, and querying the index with a vector of a different dimensions returns an error.

It is recommended to provide dimensions when creating a vector index.

Accepted values: INTEGER between 1 and 4096 inclusively.
Default value: None.

`vector.similarity_function`

The name of the similarity function used to assess the similarity of two vectors. To read more about the available similarity functions, see Cosine and Euclidean similarity functions.

Accepted values: STRING: 'cosine', 'euclidean'.
Default value: 'cosine'.

`vector.quantization.enabled`

Quantization is a technique to reduce the size of vector representations. Enabling quantization can accelerate search performance but can slightly decrease accuracy. It is recommended to enable quantization on machines with limited memory.

Accepted values: BOOLEAN: true, false.
Default value: true

Advanced configuration settings

`vector.hnsw.m`

The M parameter controls the maximum number of connections each node has in the HNSW (Hierarchical Navigable Small Worlds) graph. Increasing this value may lead to greater accuracy at the expense of increased index population and update times, especially for vectors with high dimensionality.

Accepted values: INTEGER between 1 and 512 inclusively.
Default value: 16

`vector.hnsw.ef_construction`

The number of nearest neighbors tracked during the insertion of vectors into the HNSW graph. Increasing this value increases the quality of the index, and may lead to greater accuracy (with diminishing returns) at the expense of increased index population and update times.

Accepted values: INTEGER between 1 and 3200 inclusively.
Default value: 100

Query vector indexes

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

An index cannot be used while its state is POPULATING, which occurs immediately after it is created. To check the state of a vector index — whether it is ONLINE (usable) or POPULATING (still being built; the populationPercent column shows the progress of the index creation) — run the following command: SHOW VECTOR INDEXES.

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 |
| "Goodfellas"               | "Henry Hill and his friends work their way up through the mob hierarchy."                                                                                                                                                | 0.9300689697265625 |
| "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 |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

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

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.hnsw.m`: 16, `vector.quantization.enabled`: TRUE, `vector.similarity_function`: "COSINE", `vector.hnsw.ef_construction`: 100}, indexProvider: "vector-2.0"} | ""             | "CREATE VECTOR INDEX `moviePlots` FOR (n:`Movie`) ON (n.`embedding`) OPTIONS {indexConfig: {`vector.dimensions`: 1536,`vector.hnsw.ef_construction`: 100,`vector.hnsw.m`: 16,`vector.quantization.enabled`: true,`vector.similarity_function`: 'COSINE'}}"   |
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

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. 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 index provider is vector-2.0. As of Neo4j 5.26, it is no longer possible to specify an index provider when creating indexes. Instead, Neo4j selects the most performant provider (currently vector-2.0).

Previously created vector-1.0 indexes will continue to function.

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.

Cosine and Euclidean 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
Cosine	`"cosine"`	angle
Euclidean	`"euclidean"`	distance

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

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.^[2]
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.

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.

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.

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.
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
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

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.

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

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

Limitations and known issues

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.
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

The creation of a vector index using the legacy procedure db.index.vector.createNodeIndex may fail with an error in Neo4j 5.18 and later if the database was last written to with a version prior to Neo4j 5.11, and the legacy procedure is the first write operation used on the newer version. In Neo4j 5.20, the error was clarified.

Using the CREATE VECTOR INDEX command instead avoids this issue. If the use of the procedure is unavoidable, performing any other write operation to the database on the newer binary before using the procedure will avoid the issue

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>.

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