diff --git a/modules/ROOT/pages/indexes/semantic-indexes/vector-indexes.adoc b/modules/ROOT/pages/indexes/semantic-indexes/vector-indexes.adoc index 55364da59..251898d8b 100644 --- a/modules/ROOT/pages/indexes/semantic-indexes/vector-indexes.adoc +++ b/modules/ROOT/pages/indexes/semantic-indexes/vector-indexes.adoc @@ -5,7 +5,7 @@ = Vector indexes -_Node vector search indexes were released as a public beta in Neo4j 5.11 and general availability in Neo4j 5.13._ +_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. @@ -33,38 +33,38 @@ Dump files can be imported for both link:{neo4j-docs-base-uri}/aura/auradb/impor [NOTE] The dump file used to load the dataset contains embeddings generated by https://openai.com/[OpenAI], using the model `text-embedding-ada-002`. -The examples on this page will 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 about these topics can be found in the link:https://neo4j.com/docs/genai/tutorials/embeddings-vector-indexes/[GenAI documentation - Embeddings & Vector indexes tutorial]. +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 link:https://neo4j.com/docs/genai/tutorials/embeddings-vector-indexes/[GenAI documentation - Embeddings & Vector indexes tutorial]. [[embeddings]] == Vectors and embeddings in Neo4j -Vector indexes allow users to query vector embeddings from large datasets. +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 https://cloud.google.com/vertex-ai[Vertex AI] or https://openai.com/[OpenAI]) and open source (such as https://github.com/UKPLab/sentence-transformers[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` properties on a node or relationship, where each dimensional component of the vector is an element in the `LIST`. +In Neo4j, vector embeddings are stored as `LIST` 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."` +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 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that are semantically similar or related are often represented by vectors that are closer to each other in this vector space. +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" might be close to the vector representation of "queen." -In other words, vector embeddings can be said to be a numerical representation of a particular data object, capturing its 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 write queries that match 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. +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-index]] == 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` 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 dimensionalities. +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 xref:indexes/semantic-indexes/vector-indexes.adoc#vector-index-providers[]. A vector index is created by using the `CREATE VECTOR INDEX` command. @@ -73,7 +73,8 @@ 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 ...`. [NOTE] -The index name must be unique among both indexes and constraints. +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. [TIP] Creating indexes requires link:{neo4j-docs-base-uri}/operations-manual/{page-version}/authentication-authorization/database-administration/#access-control-database-administration-index[the `CREATE INDEX` privilege]. @@ -102,13 +103,10 @@ To read more about the available similarity functions, see xref:indexes/semantic [NOTE] Prior to Neo4j 5.15, node vector indexes were created using the link:{neo4j-docs-base-uri}/operations-manual/{page-version}/reference/procedures/#procedure_db_index_vector_createnodeindex[`db.index.vector.createNodeIndex`] procedure. -[NOTE] -A newly created index is not immediately available but is created in the background. - 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 label:new[Introduced in 5.18] -[source, syntax] +[source, cypher, role=test-skip] ---- CREATE VECTOR INDEX name IF NOT EXISTS FOR ()-[r:REL_TYPE]-() ON (r.embedding) @@ -130,7 +128,7 @@ db.index.vector.queryNodes(indexName :: STRING, numberOfNearestNeighbours :: INT ---- * The `indexName` refers to the unique name of the vector index to query. -* The `numberOfNearestNeighbours` refers to the number of nearest neighbors to return as the neighborhood. +* The `numberOfNearestNeighbours` refers to the number of nearest neighbors to return. * The `query` vector refers to the `LIST` in which to search for the neighborhood. The procedure returns the neighborhood of nodes with their respective similarity scores, ordered by those scores. @@ -146,7 +144,7 @@ RETURN movie.title AS title, movie.plot AS plot, score ---- .Result -[role=queryresult] +[source, role=queryresult] ---- +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | title | plot | score | @@ -171,7 +169,7 @@ To query a relationship vector index, use the link:{neo4j-docs-base-uri}/operati db.index.vector.queryRelationships(indexName :: STRING, numberOfNearestNeighbours :: INTEGER, query :: ANY) :: (relationship :: RELATIONSHIP, score :: FLOAT) ---- -`db.index.queryRelationships` has the same argument descriptions as `db.index.queryNodes`. +`db.index.vector.queryRelationships` has the same argument descriptions as `db.index.vector.queryNodes`. [TIP] Use xref:functions/vector.adoc[] to compute the similarity score between two specific vector pairs without using a vector index. @@ -197,14 +195,17 @@ This is the same xref:indexes/search-performance-indexes/managing-indexes.adoc#l [TIP] Listing indexes requires link:{neo4j-docs-base-uri}/operations-manual/{page-version}/authentication-authorization/database-administration/#access-control-database-administration-index[the `SHOW INDEX` privilege]. -.Show all vector indexes in a database +.Show all vector indexes +==== + +.Show vector indexes with the default return columns [source, cypher, test-exclude-cols=id] ---- SHOW VECTOR INDEXES ---- .Result -[role=queryresult] +[source, role=queryresult] ---- +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | id | name | state | populationPercent | type | entityType | labelsOrTypes | properties | indexProvider | owningConstraint | lastRead | readCount | @@ -212,17 +213,23 @@ SHOW VECTOR INDEXES | 2 | "moviePlots" | "ONLINE" | 100.0 | "VECTOR" | "NODE" | ["Movie"] | ["embedding"] | "vector-2.0" | NULL | 2024-05-07T09:19:09.225Z | 47 | +----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ ---- +==== -To return full vector index details, use the `YIELD` clause. +For a full description of all return columns, see xref:indexes/search-performance-indexes/managing-indexes#listing-indexes-result-columns[Search-performance indexes → Result columns for listing indexes]. -.Show all vector indexes and all return columns +.Show vector indexes with full or filtered details +==== + +To return full vector index details, use `YIELD *`. + +.Show all vector indexes with all return columns [source, cypher, test-exclude-cols=id] ---- SHOW VECTOR INDEXES YIELD * ---- .Result -[role=queryresult] +[source, role=queryresult] ---- +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | id | name | state | populationPercent | type | entityType | labelsOrTypes | properties | indexProvider | owningConstraint | lastRead | readCount | trackedSince | options | failureMessage | createStatement | @@ -231,16 +238,16 @@ SHOW VECTOR INDEXES YIELD * +---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ ---- -It also possible to only return specific columns: +To return only specific details, specify the desired column name(s) after the `YIELD` clause. -.Show all vector indexes and specific return columns +.Show all vector indexes with filtered return columns [source, cypher] ---- SHOW VECTOR INDEXES YIELD name, type, entityType, labelsOrTypes, properties ---- .Result -[role=queryresult] +[source, role=queryresult] ---- +----------------------------------------------------------------------+ | name | type | entityType | labelsOrTypes | properties | @@ -248,9 +255,7 @@ SHOW VECTOR INDEXES YIELD name, type, entityType, labelsOrTypes, properties | "moviePlots" | "VECTOR" | "NODE" | ["Movie"] | ["embedding"] | +----------------------------------------------------------------------+ ---- - -For a full description of all return columns, see xref:indexes/search-performance-indexes/managing-indexes.adoc#listing-indexes-result-columns[Search-performance indexes -> Result columns for listing indexes]. - +==== [[drop-vector-indexes]] == Drop vector indexes @@ -298,7 +303,7 @@ Single-type, single-property index for relationships. | `LIST` | `LIST` -| Indexed vector dimensionality +| Indexed vector dimension | `INTEGER` between `1` and `2048` _inclusive_. | `INTEGER` between `1` and `4096` _inclusive_. @@ -393,7 +398,7 @@ In the above equation the trigonometric cosine is given by the scalar product of | Create node vector index. | link:{neo4j-docs-base-uri}/operations-manual/{page-version}/reference/procedures/#procedure_db_index_vector_createnodeindex[`db.index.vector.createNodeIndex`] -| Create a vector index for the specified label and property with the given vector dimensionality using the given similarity function. +| 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. @@ -430,7 +435,7 @@ It does, however, still contain some limitations and known issues. [%collapsible] ==== * 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, such as finding a local extremum _vs_ the true extremum. +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. @@ -529,11 +534,11 @@ The procedures are still usable, just not visible. | 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 dimensionality to `2048`. +| The creation of the vector index skipped the check to limit the dimension to `2048`. [NOTE] -- -Vector indexes configured with dimensionality greater than `2048` in Neo4j 5.11 should continue to work after the limitation is applied. +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