cypher 25 information

Author: JPryce-Aklundh

modules/ROOT/pages/functions/index.adoc

@@ -815,7 +815,7 @@ Vector functions allow you to compute the similarity scores of vector pairs.
 
 1.1+| xref::functions/vector.adoc#functions-vector[`vector()`]
 | `vector(vectorValue :: STRING \| LIST<INTEGER \| FLOAT>, dimension :: INTEGER, coordinateType :: [INTEGER64, INTEGER32, INTEGER16, INTEGER8, FLOAT64, FLOAT32]) :: VECTOR`
-| Constructs a `VECTOR` value. label:new[Introduced in Neo4j 2025.xx]
+| Constructs a `VECTOR` value. label:cypher[Cypher 25 only] label:new[Introduced in Neo4j 2025.xx]
 
 1.1+| xref::functions/vector.adoc#functions-similarity-cosine[`vector.similarity.cosine()`]
 | `vector.similarity.cosine(a :: VECTOR \| LIST<INTEGER \| FLOAT>, b :: VECTOR \| LIST<INTEGER \| FLOAT>) :: FLOAT`
@@ -827,15 +827,15 @@ Vector functions allow you to compute the similarity scores of vector pairs.
 
 1.1+| xref::functions/vector.adoc#functions-vector_dimension_count[`vector_dimension_count()`]
 | `(vector :: VECTOR) :: INTEGER`
-| Returns the dimension of a `VECTOR`. label:new[Introduced in Neo4j 2025.xx]
+| Returns the dimension of a `VECTOR`. label:cypher[Cypher 25 only] label:new[Introduced in Neo4j 2025.xx]
 
 1.1+| xref::functions/vector.adoc#functions-vector_distance[`vector_distance()`]
 | `(vector1 :: VECTOR, vector2 :: VECTOR, vectorDistanceMetric :: [EUCLIDEAN, EUCLIDEAN_SQUARED, MANHATTAN, COSINE, DOT, HAMMING]) :: FLOAT`
-| Returns a `FLOAT` representing the distance between the two vector values based on the selected `vectorDistanceMetric` algorithm. label:new[Introduced in Neo4j 2025.xx]
+| Returns a `FLOAT` representing the distance between the two vector values based on the selected `vectorDistanceMetric` algorithm. label:cypher[Cypher 25 only] label:new[Introduced in Neo4j 2025.xx]
 
 1.1+| xref::functions/vector.adoc#functions-vector_norm[`vector_norm()`]
 | `vector_norm(vector :: VECTOR, vectorDistanceMetric :: [EUCLIDEAN, MANHATTAN]) :: FLOAT`
-| Returns a `FLOAT` representing the distance between the given vector and a vector of the same dimension with all coordinates set to zero, calculated using the specified `vectorDistanceMetric`. label:new[Introduced in Neo4j 2025.xx]
+| Returns a `FLOAT` representing the distance between the given vector and a vector of the same dimension with all coordinates set to zero, calculated using the specified `vectorDistanceMetric`. label:cypher[Cypher 25 only] label:new[Introduced in Neo4j 2025.xx]
 
 |===
 

modules/ROOT/pages/functions/vector.adoc

@@ -5,7 +5,7 @@
 
 Vector functions allow you to construct xref:values-and-types/vector.adoc[`VECTOR` values], compute the similarity and distance scores of vector pairs, and calculate the size of a vector.
 
-[role=label--new-2025.xx]
+[role=label--new-Neo4j-2025.xx label--cypher-25-only]
 [[functions-vector]]
 == vector()
 
@@ -211,7 +211,7 @@ This returns the two nearest neighbors.
 ======
 
 
-[role=label--new-2025.xx]
+[role=label--new-Neo4j-2025.xx label--cypher-25-only]
 [[functions-vector_dimension_count]]
 == vector_dimension_count()
 
@@ -255,7 +255,7 @@ RETURN vector_dimension_count(vector([1, 2, 3], 3, INTEGER)) AS size
 =====
 
 
-[role=label--new-2025.xx]
+[role=label--new-Neo4j-2025.xx label--cypher-25-only]
 [[functions-vector_distance]]
 == vector_distance()
 
@@ -343,15 +343,15 @@ RETURN vector_distance(vector([1.0, 5.0, 3.0, 6.7], 4, FLOAT), vector([5.0, 2.5,
 
 =====
 
-[role=label--new-2025.xx]
+[role=label--new-Neo4j-2025.xx label--cypher-25-only]
 [[functions-vector_norm]]
 == vector_norm()
 
 .Details
 |===
 | *Syntax* 3+| `vector_norm(vector, vectorDistanceMetric)`
 | *Description* 3+|  Returns a `FLOAT` representing the distance between the given vector and an origin vector, which is a vector with the same dimension with all coordinates set to zero, calculated using the specified `vectorDistanceMetric`.
-.4+| *Arguments* | *Name* | *Type* | *Description*
+.3+| *Arguments* | *Name* | *Type* | *Description*
 | `vector` | `VECTOR` | A vector for which the norm to the origin vector will be computed.
 | `vectorDistanceMetric` | `[EUCLIDEAN, MANHATTAN]` | The vector distance algorithm to calculate the distance by.
 | *Returns* 3+| `FLOAT`

modules/ROOT/pages/genai-integrations.adoc

@@ -72,7 +72,7 @@ If you want to generate many embeddings at once, use xref:genai-integrations.ado
 ======
 
 `genai.vector.encode()` returns a `LIST<FLOAT>`.
-To convert and store this value as a xref:values-and-types/vector.adoc[`VECTOR` property type (introduced in Neo4j 2025.xx)], use the xref:functions/vector.adoc#functions-vector[`vector()`] function.
+To convert and store this value as a xref:values-and-types/vector.adoc[`VECTOR` property type], use the xref:functions/vector.adoc#functions-vector[`vector()`] function.
 Storing `VECTOR` values on an on-prem instance requires Enterprise Edition and using Neo4j's link:{neo4j-docs-base-uri}/operations-manual/current/database-internals/store-formats/#store-format-overview[block format].
 
 .Signature for `vector()` label:function[]
@@ -193,7 +193,7 @@ Each returned row contains the following columns:
 ======
 
 `genai.vector.encode()` returns a `LIST<FLOAT>`.
-To convert and store this value as a xref:values-and-types/vector.adoc[`VECTOR` property type (introduced in Neo4j 2025.xx)], use the xref:functions/vector.adoc#functions-vector[`vector()`] function.
+To convert and store this value as a xref:values-and-types/vector.adoc[`VECTOR` property type], use the xref:functions/vector.adoc#functions-vector[`vector()`] function.
 Storing `VECTOR` values on an on-prem instance requires Enterprise Edition and using Neo4j's link:{neo4j-docs-base-uri}/operations-manual/current/database-internals/store-formats/#store-format-overview[block format].
 
 .Signature for `vector()` label:function[]

modules/ROOT/pages/indexes/semantic-indexes/vector-indexes.adoc

@@ -41,7 +41,7 @@ Each word or token in a text is typically represented as high-dimensional vector
 
 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.
 Vector embeddings are stored as `LIST<INTEGER | FLOAT>` properties on a node or relationship.
-As of Neo4j 2025.xx, they can also be more efficiently stored as xref:values-and-types/vector.adoc[`VECTOR`] properties.
+As of Neo4j 2025.xx, they can also be more efficiently stored as xref:values-and-types/vector.adoc[`VECTOR`] properties using Cypher 25.
 
 [NOTE]
 ====

modules/ROOT/pages/values-and-types/index.adoc

@@ -13,7 +13,7 @@ More information about the data values and types supported by Cypher can be foun
 * xref:values-and-types/spatial.adoc[]
 * xref:values-and-types/lists.adoc[]
 * xref:values-and-types/maps.adoc[]
-* xref:values-and-types/vector.adoc[] label:new[Introduced in Neo4j 2025.xx]
+* xref:values-and-types/vector.adoc[] label:cypher[Cypher 25 only] label:new[Introduced in Neo4j 2025.xx]
 * xref:values-and-types/graph-references.adoc[]
 * xref::values-and-types/working-with-null.adoc[]
 * xref::values-and-types/casting-data.adoc[]

modules/ROOT/pages/values-and-types/ordering-equality-comparison.adoc

@@ -51,7 +51,7 @@ Values of different types are ordered based on a predefined hierarchy, from leas
 * xref::values-and-types/property-structural-constructed.adoc#structural-types[`RELATIONSHIP`]
 * xref::values-and-types/lists.adoc[`LIST`]
 * xref::patterns/fixed-length-patterns.adoc#path-patterns[`PATH`]
-* xref::values-and-types/vector.adoc[`VECTOR`] label:new[Introduced in Neo4j 2025.xx]
+* xref::values-and-types/vector.adoc[`VECTOR`] label:cypher[Cypher 25 only] label:new[Introduced in Neo4j 2025.xx]
 * xref::values-and-types/spatial.adoc[`POINT`]
 * xref::values-and-types/temporal.adoc[`ZONED DATETIME`]
 * xref::values-and-types/temporal.adoc[`LOCAL DATETIME`]

modules/ROOT/pages/values-and-types/property-structural-constructed.adoc

@@ -12,7 +12,7 @@ This section will first provide a brief overview of each type, and then go into
 
 A property type value is one that can be stored as a node or relationship property.
 
-Property types are the most primitive types in Cypher and include the following: `BOOLEAN`, `DATE`, `DURATION`, `FLOAT`, `INTEGER`, `LIST`, `LOCAL DATETIME`, `LOCAL TIME`, `POINT`, `STRING`, `VECTOR` (introduced in Neo4j 2025.xx) `ZONED DATETIME`, and `ZONED TIME`.
+Property types are the most primitive types in Cypher and include the following: `BOOLEAN`, `DATE`, `DURATION`, `FLOAT`, `INTEGER`, `LIST`, `LOCAL DATETIME`, `LOCAL TIME`, `POINT`, `STRING`, `VECTOR`, `ZONED DATETIME`, and `ZONED TIME`.
 
 * Property types can be returned from Cypher queries.
 * Property types can be used as xref::syntax/parameters.adoc[parameters].
@@ -100,7 +100,7 @@ However, not all types can be used in all places.
 | `PROPERTY VALUE` | `ANY PROPERTY VALUE`
 | `RELATIONSHIP` | `ANY RELATIONSHIP`, `EDGE`, `ANY EDGE`
 | `STRING` | `VARCHAR`
-| `VECTOR<TYPE>(DIMENSION)` label:new[Introduced in Neo4j 2025.xx] | `VECTOR`, `VECTOR<TYPE>`, `VECTOR(DIMENSION)`, `VECTOR(DIMENSION, TYPE)`
+| `VECTOR<TYPE>(DIMENSION)` | `VECTOR`, `VECTOR<TYPE>`, `VECTOR(DIMENSION)`, `VECTOR(DIMENSION, TYPE)`
 | `ZONED DATETIME` | `TIMESTAMP WITH TIME ZONE`, `TIMESTAMP WITH TIMEZONE`
 | `ZONED TIME` | `TIME WITH TIME ZONE`, `TIME WITH TIMEZONE`
 | `INNER_TYPE_1 \| INNER_TYPE_2...` | `ANY<INNER_TYPE_1 \| INNER_TYPE_2...>`

modules/ROOT/pages/values-and-types/vector.adoc

@@ -1,6 +1,6 @@
 = Vectors
 :description: Information about Cypher's `VECTOR` type.
-:page-role: new-2025.xx
+:page-role: cypher-25-only new-neo4j-2025.xx
 
 
 Cypher supports the construction of a `VECTOR` value that can be stored as xref:indexes/semantic-indexes/vector-indexes.adoc#embeddings[embedding] properties on nodes and relationships and utilized for efficient semantic retrieval using Neo4j's xref:indexes/semantic-indexes/vector-indexes.adoc[vector indexes] and xref:genai-integrations.adoc[GenAI plugin].