--- title: "Neo4j vector indexes" url: https://memory.wiki/_l0p4GDf updated: 2026-10-09T20:36:42.140Z hub: https://memory.wiki/hub/pratofeito bundle_count: 1 concept_count: 12 source: "ios" --- # Neo4j vector indexes --- title: "Vector indexes - Cypher Manual"\ source: ""\ author:\ published:\ created: 2026-10-09\ description: "Information about creating, querying, and deleting vector indexes with Cypher."\ tags: - "clippings" --- ## Vector indexes Vector indexes enable similarity searches and complex analytical queries by representing nodes or properties as vectors in a multidimensional space. Vector search is often used on its own for semantic matching, but it can also be combined with full-text search or other ranked result sources for hybrid search. For a worked example, see [Developer Guide → Hybrid search](https://neo4j.com/developer/genai-ecosystem/hybrid-search/). 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](https://lucene.apache.org/) indexing and search library.[\[1\]](#fn1) ## Example graph The examples on this page use the [Neo4j movie recommendations](https://github.com/neo4j-graph-examples/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. ![Graph example connecting movie to person nodes via acted in and directed relationships](https://neo4j.com/docs/cypher-manual/current/_images/vector-index-graph.svg)The graph contains 28863 nodes and 332522 relationships. To recreate the graph, download and import this [recommendations-5.26.dump file](https://github.com/neo4j-graph-examples/recommendations/raw/refs/heads/main/data/recommendations-embeddings-aligned-5.26.dump) to an empty Neo4j database. - Aura instances: select *Restore from file* and drag and drop the dump file. For more information, see [Aura Documentation → Restore from backup file](https://neo4j.com/docs/aura/managing-instances/backup-restore-export/#restore-backup). - On-prem instances: Use the `neo4j-admin database load` command to import the dump file and the `neo4j-admin database migrate` command to migrate it to the Neo4j version your instance is running. Then create a new database with the **same name** as the dump file. ```js bin/neo4j-admin database load recommendations-5.26 --from-path=./ bin/neo4j-admin database migrate recommendations-5.26 bin/cypher-shell -u neo4j -p -d system 'CREATE DATABASE \`recommendations-5.26\` WAIT' ``` | | The dump file used to load the dataset contains embeddings generated by [OpenAI](https://openai.com/), 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](https://cloud.google.com/vertex-ai) or [OpenAI](https://openai.com/)) and open source (such as [sentence-transformers](https://github.com/huggingface/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](https://neo4j.com/docs/graph-data-science/current/machine-learning/node-embeddings/) 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` properties on a node or relationship. As of Neo4j 2025.10, they can also be more efficiently stored as `VECTOR` properties using Cypher® 25. Vector indexes are available in both Neo4j Enterprise Edition and Community Edition. Community Edition can index embeddings stored as `LIST` properties. Storing embeddings as native `VECTOR` properties requires [block format](https://neo4j.com/docs/operations-manual/current/database-internals/store-formats/#store-format-overview), which is available in Enterprise Edition and Aura. | | For information about how embeddings can be generated and stored as properties, see: - [GenAI documentation → Create and store embeddings in a Neo4j database](https://neo4j.com/docs/genai/plugin/current/embeddings/) - [GenAI documentation → Embeddings & Vector Indexes Tutorial](https://neo4j.com/docs/genai/tutorials/embeddings-vector-indexes/) | | --- | --- | 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: ```js [0.005967312026768923, -0.03817005082964897, 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-0.015104657039046288, -0.01216800045222044, 0.0034552712459117174, -0.003481202060356736, 0.013561777770519257, 0.0022592158056795597, -0.01183090079575777, 0.013665501028299332, 0.017075393348932266, 0.008511765860021114, -0.007409709505736828, -0.0014261907199397683, -0.003058206755667925, 0.0031424816697835922, 0.0021846650633960962, -0.000989419873803854, -0.022144854068756104, 0.0036270625423640013, -0.006233102176338434, -0.016504917293787003, 0.003999816719442606, 0.017931107431650162, -0.025956671684980392, 0.021626237779855728, 0.002192768268287182, -0.001260882243514061, 0.018721995875239372, -0.023493250831961632, 0.030598275363445282, -0.011616972275078297, -0.019331367686390877, 0.01505279541015625, 0.01505279541015625, 0.004524914547801018, 0.0019042887724936008, -0.014962038025259972] ``` 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 created by using the `CREATE VECTOR INDEX [index_name]` 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 …`. | Neo4j version | Capabilities | | --- | --- | | **5.13 (general availability)** | Supports single-label, single-property index for nodes or single-relationship-type, single-property index for relationships. Vectors stored as `LIST ` properties. | | **2025.10** | Adds support for `VECTOR` value properties validated against index dimensions and the configured vector similarity function. | | **2026.01** | Adds support for multi-label and multi-relationship-type vector indexes with additional properties used for filtering. These properties can later be used in [vector search with filters using ](https://neo4j.com/docs/cypher-manual/current/clauses/search/#vector-search-filtering)`SEARCH … WHERE`, but they cannot be vector properties. A vector index can store only one vector property per node or relationship. | | **2026.07** | Adds support for High Fidelity Quantized (HFQ) vector search, which can be enabled [in the index configuration](#configuration-settings). | | | 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 ](https://neo4j.com/docs/operations-manual/current/authentication-authorization/database-administration/#access-control-database-administration-index)`CREATE INDEX`[ privilege](https://neo4j.com/docs/operations-manual/current/authentication-authorization/database-administration/#access-control-database-administration-index). | | --- | --- | ```cypher CREATE VECTOR INDEX moviePlots IF NOT EXISTS FOR (m:Movie) ON m.embedding OPTIONS { indexConfig: { \`vector.dimensions\`: 1536, \`vector.similarity_function\`: 'cosine' }} ``` 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. To read more about the available configuration settings, see [Configuration settings](#configuration-settings). In the above example, the vector dimension is explicitly set to `1536`. Providing dimensions when creating a vector index is recommended, as it ensures that only vectors of that size are indexed and makes dimension mismatches fail clearly at query time. The vector similarity function is set 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](#similarity-functions). You can also create a vector index for relationships with a particular type on a given property using the following syntax: ```cypher CREATE VECTOR INDEX name IF NOT EXISTS FOR ()-[r:REL_TYPE]-() ON (r.embedding) OPTIONS { indexConfig: { \`vector.dimensions\`: $dimension, \`vector.similarity_function\`: $similarityFunction }} ``` Like full-text indexes, vector indexes may be created for multiple labels or relationship types. In that case, all nodes with *at least one* label matching *at least one* of the labels or a relationship whose type matches *at least one* of the relationship types are included as long as they have the vector property. In contrast to multi-property full-text indexes, the vector property is the only property to determine if the node or relationship is included in the index. The additional properties will only be included in the index if they are of one of the types `INTEGER`, `FLOAT`, `STRING`, `BOOLEAN`, `DATE`, `ZONED DATETIME`, `LOCAL DATETIME`, `ZONED TIME`, `LOCAL TIME` or `DURATION`. If the node or relationship does not have an additional property or if the additional property is of another type, it is treated as `null` by the index. You can create a vector index on multiple labels with additional properties for filtering using the following query: ```cypher CREATE VECTOR INDEX multiLabelAdditionalProperties IF NOT EXISTS FOR (n:Movie|Actor) ON n.embedding WITH [n.title, n.plot, n.name, n.born] OPTIONS { indexConfig: { \`vector.dimensions\`: 1536, \`vector.similarity_function\`: 'cosine' }} ``` And similarly for relationships on multiple relationship types with additional properties using the following syntax: ```cypher CREATE VECTOR INDEX name IF NOT EXISTS FOR ()-[r:REL_TYPE1|REL_TYPE2|REL_TYPE3]-() ON (r.embedding) WITH [r.property] 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-index-providers). #### vector.dimensions The dimensions of the vectors to be indexed. For more information, see [Vectors and embeddings in Neo4j](#embeddings). This setting can be omitted, and any `LIST` or, as of Neo4j 2025.10, additionally `VECTOR` value 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 dimension returns an error. | | It is recommended to provide dimensions when creating a vector index. Doing so avoids the confusing situation where the same index may contain side-by-side vectors of different dimensions, while still only allowing comparisons between vectors of the same dimension. | | --- | --- | 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](#similarity-functions). Accepted values `STRING`: `'cosine'`, `'euclidean'`. Default value `'cosine'`. #### vector.default_search_expansion_factorIntroduced in Neo4j 2026.07 Scales the number of nearest neighbors to query the vector index, while returning the originally requested number of nearest neighbors. Setting this value above `1.0` can increase accuracy at the cost of query time. Accepted values `FLOAT`: between `1.0` and `10_000.0` *inclusive*. Default values Dependent upon the value of `vector.quantization.type`. | Value of `vector.quantization.type` | Default value `vector.default_search_expansion_factor` | | --- | --- | | `'none'` | `1.0` | | `'scalar'` | `1.5` | | `'binary'` | `3.0` | #### vector.quantization.enabledDeprecated in Neo4j 2026.06 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` #### vector.quantization.typeIntroduced in Neo4j 2026.06 Quantization is a technique to reduce the size of vector representations. Setting a quantization type can accelerate search performance but decrease accuracy. Binary quantization is a more aggressive form of Scalar quantization, reducing each dimension to a single bit. If `vector.default_search_expansion_factor` is set larger than `1.0` with binary quantization, the returned nearest neighbors are rescored with their unquantized vector values to increase accuracy (High Fidelity Quantized vector search). It is recommended to use quantization on machines with limited memory. Accepted values `STRING`: `'none'`, `'scalar'`, `'binary'`. Default value `'binary'` | | Prior to the release of Neo4j 2026.08, the default value for this setting was `'scalar'`. | | --- | --- | ### 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 As of Neo4j 2026.01, the preferred way of querying vector indexes is by using the Cypher [SEARCH](https://neo4j.com/docs/cypher-manual/current/clauses/search/) clause. A less powerful alternative, which also works for earlier Neo4j versions, is using the procedures [db.index.vector.queryNodes](https://neo4j.com/docs/operations-manual/current/procedures/built-in-procedures/#procedure_db_index_vector_queryNodes) and [db.index.vector.queryRelationships](https://neo4j.com/docs/operations-manual/current/procedures/built-in-procedures/#procedure_db_index_vector_queryRelationships). These procedures are deprecated as of Neo4j 2026.04. Vector index scores are meaningful within the result set from a vector query. When combining vector results with full-text results or other scored sources, rank each source independently rather than comparing raw score values directly. For more information, see [Developer Guide → Hybrid search](https://neo4j.com/developer/genai-ecosystem/hybrid-search/). | | 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`. | | --- | --- | ### Querying a node vector index using the SEARCH clause The `SEARCH` clause returns the neighborhood of nodes, optionally with their respective similarity scores, ordered by those scores. The scores are bounded between `0.0` and `1.0`, where the closer to `1.0` the score is, the more similar the indexed vector is to the query vector. ```cypher MATCH (m:Movie {title: 'Godfather, The'}) MATCH (movie: Movie) SEARCH movie IN ( VECTOR INDEX moviePlots FOR m.embedding LIMIT 5 ) SCORE AS score RETURN movie.title AS title, movie.plot AS plot, score ``` Result ```js +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | title | plot | score | +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | "Godfather, The" | "The aging patriarch of an organized crime dynasty transfers control of his clandestine empire to his reluctant son." | 0.9999999403953552 | | "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." | 0.9648237824440002 | | "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." | 0.954778790473938 | | "Scarface" | "An ambitious and near insanely violent gangster climbs the ladder of success in the mob, but his weaknesses prove to be his downfall." | 0.9367182850837708 | | "Jane Austen's Mafia!" | "Takeoff on the Godfather with the son of a mafia king taking over for his dying father" | 0.9366796016693115 | +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ ``` All movies returned have a plot centered around criminal family organizations. The `score` results are returned in *descending order*, where the best matching result entry is put first. `The Godfather` has a similarity score of `1.0` because the index was queried with this specific property. To exclude the query vector, add the predicate `WHERE score < 1`. ### Querying a node vector index using the db.index.vector.queryNodes() procedure ```syntax 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` refers to the `LIST` or, as of Neo4j 2025.10, additionally the `VECTOR` of which the neighborhood should be searched. 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. ```cypher 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 ```js +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | title | plot | score | +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | "Godfather, The" | "The aging patriarch of an organized crime dynasty transfers control of his clandestine empire to his reluctant son." | 0.9999999403953552 | | "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." | 0.9648237824440002 | | "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." | 0.954778790473938 | | "Scarface" | "An ambitious and near insanely violent gangster climbs the ladder of success in the mob, but his weaknesses prove to be his downfall." | 0.9367182850837708 | | "Jane Austen's Mafia!" | "Takeoff on the Godfather with the son of a mafia king taking over for his dying father" | 0.9366796016693115 | +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ ``` 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. ### Querying a relationship vector index To query a relationship vector index, use the Cypher [SEARCH](https://neo4j.com/docs/cypher-manual/current/clauses/search/) clause or the `db.index.vector.queryRelationships` procedure in a similar manner as shown for node vector indexes above. ```syntax 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](https://neo4j.com/docs/cypher-manual/current/functions/vector/) 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 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](https://neo4j.com/docs/operations-manual/current/configuration/configuration-settings/#config_server.jvm.additional): Configuration settings ```config server.jvm.additional=--add-modules=jdk.incubator.vector ``` Starting with Neo4j 2026.08, the `jdk.incubator.vector` module is enabled by default. For details, see the [Operations manual → Changes in Neo4j 2025-2026 series](https://neo4j.com/docs/operations-manual/current/changes-2025-2026/#vector-api-changes). | | While the Neo4j 5 series requires at least Java 17, *at least* Java 20 is necessary to take advantage of this performance improvement in Neo4j 5.14-5.26. If you are using Neo4j 2025.01 or later, Java 21 is required. For more information about what Java versions are supported by different Neo4j versions, see the [Operations Manual → System requirements → Java](https://neo4j.com/docs/operations-manual/current/installation/requirements/#deployment-requirements-java). | | --- | --- | ## Show vector indexes To show all vector indexes in a database, use the `SHOW VECTOR INDEXES` command. This is the same `SHOW`[ command as for other indexes](https://neo4j.com/docs/cypher-manual/current/indexes/performance-indexes/list-indexes/), with the index type filtering on `VECTOR`. | | Showing indexes requires [the ](https://neo4j.com/docs/operations-manual/current/authentication-authorization/database-administration/#access-control-database-administration-index)`SHOW INDEX`[ privilege](https://neo4j.com/docs/operations-manual/current/authentication-authorization/database-administration/#access-control-database-administration-index). | | --- | --- | Example 1. Show all vector indexes ```cypher SHOW VECTOR INDEXES ``` Result ```js +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | id | name | state | populationPercent | type | entityType | labelsOrTypes | properties | indexProvider | owningConstraint | lastRead | readCount | +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | 1 | "moviePlots" | "ONLINE" | 100.0 | "VECTOR" | "NODE" | ["Movie"] | ["embedding"] | "vector-2026.08" | NULL | 2026-08-24T12:10:35.169Z | 2 | | 2 | "multiLabelAdditionalProperties" | "ONLINE" | 100.0 | "VECTOR" | "NODE" | ["Movie", "Actor"] | ["embedding", "title", "plot", "name", "born"] | "vector-2026.08" | NULL | NULL | 0 | +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ ``` For a full description of all return columns, see [Performance indexes → Show indexes → Return columns](https://neo4j.com/docs/cypher-manual/current/indexes/performance-indexes/list-indexes/#show-indexes-result-columns). Example 2. Show vector indexes with full or filtered details To return full vector index details, use `YIELD *`. ```cypher SHOW VECTOR INDEXES YIELD * ``` Result ```js +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | id | name | state | populationPercent | type | entityType | labelsOrTypes | properties | indexProvider | owningConstraint | lastRead | readCount | trackedSince | options | failureMessage | createStatement | +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | 1 | "moviePlots" | "ONLINE" | 100.0 | "VECTOR" | "NODE" | ["Movie"] | ["embedding"] | "vector-2026.08" | NULL | 2026-08-24T12:10:35.169Z | 2 | 2026-08-24T12:08:41.009Z | {indexConfig: {\`vector.dimensions\`: 1536, \`vector.default_search_expansion_factor\`: 3.0, \`vector.hnsw.m\`: 16, \`vector.quantization.type\`: "BINARY", \`vector.similarity_function\`: "COSINE", \`vector.hnsw.ef_construction\`: 100}} | "" | "CREATE VECTOR INDEX \`moviePlots\` FOR (n:\`Movie\`) ON (n.\`embedding\`) OPTIONS {indexConfig: {\`vector.default_search_expansion_factor\`: 3.0,\`vector.dimensions\`: 1536,\`vector.hnsw.ef_construction\`: 100,\`vector.hnsw.m\`: 16,\`vector.quantization.type\`: 'BINARY',\`vector.similarity_function\`: 'COSINE'}}" | | 2 | "multiLabelAdditionalProperties" | "ONLINE" | 100.0 | "VECTOR" | "NODE" | ["Movie", "Actor"] | ["embedding", "title", "plot", "name", "born"] | "vector-2026.08" | NULL | NULL | 0 | 2026-08-24T12:08:53.602Z | {indexConfig: {\`vector.dimensions\`: 1536, \`vector.default_search_expansion_factor\`: 3.0, \`vector.hnsw.m\`: 16, \`vector.quantization.type\`: "BINARY", \`vector.similarity_function\`: "COSINE", \`vector.hnsw.ef_construction\`: 100}} | "" | "CREATE VECTOR INDEX \`multiLabelAdditionalProperties\` FOR (n:\`Movie\`|\`Actor\`) ON (n.\`embedding\`) WITH [n.\`title\`, n.\`plot\`, n.\`name\`, n.\`born\`] OPTIONS {indexConfig: {\`vector.default_search_expansion_factor\`: 3.0,\`vector.dimensions\`: 1536,\`vector.hnsw.ef_construction\`: 100,\`vector.hnsw.m\`: 16,\`vector.quantization.type\`: 'BINARY',\`vector.similarity_function\`: 'COSINE'}}" | +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ ``` To return only specific details, specify the desired column name(s) after the `YIELD` clause. ```cypher SHOW VECTOR INDEXES YIELD name, type, entityType, labelsOrTypes, properties ``` Result ```js +--------------------------------------------------------------------------------------------------------------------------------+ | name | type | entityType | labelsOrTypes | properties | +--------------------------------------------------------------------------------------------------------------------------------+ | "moviePlots" | "VECTOR" | "NODE" | ["Movie"] | ["embedding"] | | "multiLabelAdditionalProperties" | "VECTOR" | "NODE" | ["Movie", "Actor"] | ["embedding", "title", "plot", "name", "born"] | +--------------------------------------------------------------------------------------------------------------------------------+ ``` ## Drop vector indexes A vector index is dropped by using the [same command as for other indexes](https://neo4j.com/docs/cypher-manual/current/indexes/performance-indexes/drop-indexes/), `DROP INDEX`. It is possible to drop vector indexes created in a different Cypher version than the one in use. For example, although the multiple labels or additional properties were added in Cypher 25, such indexes can still be dropped using Cypher 5. The index name can also be given as a parameter when dropping an index: `DROP INDEX $name`. | | Dropping indexes requires [the ](https://neo4j.com/docs/operations-manual/current/database-administration/authentication-authorization/database-administration/#access-control-database-administration-index)`DROP INDEX`[ privilege](https://neo4j.com/docs/operations-manual/current/database-administration/authentication-authorization/database-administration/#access-control-database-administration-index). | | --- | --- | ```cypher DROP INDEX moviePlots ``` ## Vector index providers for compatibility Neo4j automatically selects the latest provider when creating a vector index: it is not possible to specify an index provider explicitly in Cypher 25. Indexes created with older providers will continue to function. ```cypher SHOW VECTOR INDEXES YIELD name, type, indexProvider ``` Learn more about the `vector-2026.08` provider **(latest)** Introduced in Neo4j 2026.08 | Feature | Capability | | --- | --- | | Index schema | multi-label, multi-property index for nodes. multi-type, multi-property index for relationships. Only one vector property can be stored per node or relationship. Any additional properties included in the index must be one of the following types: `INTEGER`, `FLOAT`, `STRING`, `BOOLEAN`, `DATE`, `ZONED DATETIME`, `LOCAL DATETIME`, `ZONED TIME`, `LOCAL TIME` or `DURATION`. | | Indexed property value type | `VECTOR` or `LIST`. | | Indexed vector dimension | `INTEGER` between `1` and `4096` *inclusive*. | | Indexed vector quantization | High Fidelity Quantized vector search for binary quantization. `'binary'` is the default quantization type. | | Indexed hnsw settings | Supported | | [Cosine similarity vector validity](#similarity-functions) | 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. | Learn more about the `vector-2026.07` provider Introduced in Neo4j 2026.07 | Feature | Capability | | --- | --- | | Index schema | multi-label, multi-property index for nodes. multi-type, multi-property index for relationships. Only one vector property can be stored per node or relationship. Any additional properties included in the index must be one of the following types: `INTEGER`, `FLOAT`, `STRING`, `BOOLEAN`, `DATE`, `ZONED DATETIME`, `LOCAL DATETIME`, `ZONED TIME`, `LOCAL TIME` or `DURATION`. | | Indexed property value type | `VECTOR` or `LIST`. | | Indexed vector dimension | `INTEGER` between `1` and `4096` *inclusive*. | | Indexed vector quantization | High Fidelity Quantized vector search for scalar and binary quantization. | | Indexed hnsw settings | Supported | | [Cosine similarity vector validity](#similarity-functions) | 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. | Learn more about the `vector-2026.06` provider Introduced in Neo4j 2026.06 | Feature | Capability | | --- | --- | | Index schema | multi-label, multi-property index for nodes. multi-type, multi-property index for relationships. Only one vector property can be stored per node or relationship. Any additional properties included in the index must be one of the following types: `INTEGER`, `FLOAT`, `STRING`, `BOOLEAN`, `DATE`, `ZONED DATETIME`, `LOCAL DATETIME`, `ZONED TIME`, `LOCAL TIME` or `DURATION`. | | Indexed property value type | `VECTOR` or `LIST`. | | Indexed vector dimension | `INTEGER` between `1` and `4096` *inclusive*. | | Indexed vector quantization | Scalar and binary quantization supported. | | Indexed hnsw settings | Supported | | [Cosine similarity vector validity](#similarity-functions) | 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. | Learn more about the `vector-3.0` provider Introduced in Neo4j 2025.09 | Feature | Capability | | --- | --- | | Index schema | multi-label, multi-property index for nodes. multi-type, multi-property index for relationships. Only one vector property can be stored per node or relationship. Any additional properties included in the index must be one of the following types: `INTEGER`, `FLOAT`, `STRING`, `BOOLEAN`, `DATE`, `ZONED DATETIME`, `LOCAL DATETIME`, `ZONED TIME`, `LOCAL TIME` or `DURATION`. | | Indexed property value type | `VECTOR` or `LIST` | | Indexed vector dimension | `INTEGER` between `1` and `4096` *inclusive*. | | Indexed vector quantization | Scalar quantization supported. | | Indexed hnsw settings | Supported | | [Cosine similarity vector validity](#similarity-functions) | 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. | Learn more about the `vector-2.0` provider Introduced in Neo4j 5.18 | Feature | Capability | | --- | --- | | Index schema | Single-label, single-property index for nodes. Single-type, single-property index for relationships. | | Indexed property value type | `VECTOR` or `LIST` | | Indexed vector dimension | `INTEGER` between `1` and `4096` *inclusive*. | | Indexed vector quantization | Scalar quantization supported. | | Indexed hnsw settings | Supported | | [Cosine similarity vector validity](#similarity-functions) | 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. | Learn more about the `vector-1.0` provider Introduced in Neo4j 5.11 | Feature | Capability | | --- | --- | | Index schema | Single-label, single-property index for nodes. *No relationship support.* | | Indexed property value type | `LIST` | | Indexed vector dimension | `INTEGER` between `1` and `2048` *inclusive*. | | Indexed vector quantization | Not supported | | Indexed hnsw settings | Not supported | | [Cosine similarity vector validity](#similarity-functions) | 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. | ## 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. | 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.[\[2\]](#fn2) - 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.](https://neo4j.com/docs/cypher-manual/current/_images/cosine-similarity-equation.svg)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.](https://neo4j.com/docs/cypher-manual/current/_images/euclidean-similarity-equation.svg)## Vector index procedures | Usage | Procedure | Description | | --- | --- | --- | | 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. Replaced by Cypher’s `SEARCH` clause. Deprecated in Neo4j 2026.04 | | 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. Replaced by Cypher’s `SEARCH` clause. Deprecated in Neo4j 2026.04 | | 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`. | | 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`. | ## Limitations and known issues The vector index is no longer a beta feature. It does, however, still contain some 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](#similarity-functions) and one [cosine](#similarity-functions) vector index on the same label-property key pair. - Changes made within the same transaction are not visible to the index. Vector indexes also contain some known issues. The following table lists the 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` render the vector arguments with a type of `ANY` rather than the semantically correct type of `VECTOR | LIST`. The types are still enforced as `VECTOR | LIST`. | | --- 1. [1](#_footnoteref_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](https://ieeexplore.ieee.org/document/8594636/) — Yury A. Malkov and Dmitry A. Yashunin [↩︎](#fnref1) 2. [2](#_footnoteref_2). [IEEE Standard for Floating-Point Arithmetic](https://ieeexplore.ieee.org/document/8766229) [↩︎](#fnref2) --- ## Summary Vector indexes in Neo4j allow for similarity searches and complex analytical queries by representing data as vectors in a multidimensional space. These indexes can be used for semantic matching or combined with other search methods to perform hybrid queries. ## Themes - Neo4j vector indexing - Semantic search implementation - Hybrid search capabilities - Embedding storage methods ## Key takeaways - Neo4j vector indexes are powered by the Apache Lucene library. - Vector embeddings can be stored as LIST properties or as native VECTOR properties in newer versions. - Community Edition supports indexing embeddings stored as LIST types, while native VECTOR storage requires Enterprise Edition or Aura. - Vector indexes allow for hybrid search by combining semantic matching with other ranked result sources. - Embeddings can be generated by various external providers like OpenAI or Vertex AI, or internally via Graph Data Science algorithms. ## Insights - Vector indexes are not limited to text/document semantic matching but can also capture structural graph topology when using GDS algorithms. - The distinction between storing embeddings as lists versus native vector types depends on the Neo4j edition and storage format used. - Hybrid search is achieved by combining vector similarity results with traditional full-text search or other ranked result sources. ## Open questions / gaps - What are the specific performance trade-offs between using LIST properties versus native VECTOR properties in large-scale production environments? ## Concepts in this document - **Neo4j** _(entity)_ Graph database platform that serves as the storage and retrieval foundation for knowledge graphs and hybrid search. - **GraphRAG** _(concept)_ Retrieval-augmented generation pattern that uses knowledge graphs to provide contextual information for LLM responses. - **Knowledge Graph** _(concept)_ Structured representation of entities and relationships extracted from unstructured data for contextual retrieval. - **Hybrid Search** _(concept)_ Multi-signal retrieval combining lexical, semantic, and structural search to improve result quality and coverage. - **Graph Database** _(entity)_ The technology being investigated as a foundational architecture for personal knowledge management. - **Knowledge Management** _(tag)_ Broad domain of organizing, storing, and retrieving information for personal or organizational use. - **Memory system** _(concept)_ Concept describing graph databases as a memory system for work. - **Neo4j Graph Data Science** _(entity)_ Neo4j library providing algorithms like FastRP for converting graph topology into searchable vector embeddings. - **Retrieval-Augmented Generation** _(tag)_ Domain combining information retrieval with generative AI to provide contextually grounded LLM responses. - **Vector indexes** _(concept)_ High-dimensional vector representations enabling semantic similarity in Neo4j. - **Apache Lucene** _(entity)_ Indexing and search library that powers Neo4j vector indexes. - **memory.wiki** _(entity)_ A knowledge management platform providing REST APIs, CLI tools, and MCP server integration. ## Concept relations (within this doc's concepts) - **Knowledge Management** contextualizes exploration of **Graph Database** - **GraphRAG** uses to augment **Knowledge Graph** - **GraphRAG** utilizes **Knowledge Graph** - **Neo4j** is a **Graph Database** - **Neo4j** supports **Hybrid Search** - **Neo4j** implements **Vector indexes** - **memory.wiki** hosts documents on **Neo4j** - **Vector indexes** powered by **Apache Lucene** - **Hybrid Search** improves results for **Retrieval-Augmented Generation** - **GraphRAG** shares concept **Knowledge Graph** - **Graph Database** implemented by **Neo4j** - **Memory system** implemented via **Graph Database** - **Neo4j** is implementation of **Graph Database** - **Neo4j** supports advanced **Hybrid Search** - **Neo4j** stores and manages **Knowledge Graph** - **Neo4j** is type of **Graph Database** - **Neo4j** features **Vector indexes** - **memory.wiki** hosts documentation for **Neo4j** - **GraphRAG** utilizes structure of **Knowledge Graph** - **Neo4j** supports implementation of **Hybrid Search** ## Bundles containing this document - [Neo4j and GraphRAG implementation](https://memory.wiki/b/bMK8g0yP) _Hub canonical:_ https://memory.wiki/hub/pratofeito _Concept digest:_ https://memory.wiki/raw/hub/pratofeito?digest=1&compact=1