neo4j · stefano-ottolenghi · Nov 3, 2025 · Nov 3, 2025
diff --git a/modules/ROOT/content-nav.adoc b/modules/ROOT/content-nav.adoc
@@ -107,7 +107,6 @@
 ** xref:functions/user-defined.adoc[]
 ** xref:functions/vector.adoc[]
 
-* xref:genai-integrations.adoc[]
 * xref:indexes/index.adoc[]
 ** xref:indexes/search-performance-indexes/index.adoc[]
 *** xref:indexes/search-performance-indexes/managing-indexes.adoc[]

diff --git a/modules/ROOT/pages/appendix/gql-conformance/additional-cypher.adoc b/modules/ROOT/pages/appendix/gql-conformance/additional-cypher.adoc
@@ -186,19 +186,6 @@ REMOVE n:$($label)
 
 |===
 
-[[genai-functions]]
-=== GenAI functions
-
-[options="header", cols="2a,5a"]
-|===
-| Cypher feature
-| Description
-
-| xref:genai-integrations.adoc#single-embedding[`genai.vector.encode()`]
-| Generates a vector embedding for a single value.
-
-|===
-
 
 [[graph-functions]]
 === Graph functions

diff --git a/modules/ROOT/pages/deprecations-additions-removals-compatibility.adoc b/modules/ROOT/pages/deprecations-additions-removals-compatibility.adoc
@@ -41,7 +41,7 @@ label:new[]
 VECTOR([1.05, 0.123, 5], 3, FLOAT32 NOT NULL)
 ----
 
-a| Introduced a `VECTOR` value type 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].
+a| Introduced a `VECTOR` value type 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 link:https://neo4j.com/docs/genai/plugin/[GenAI plugin].
 For more information, see xref:values-and-types/vector.adoc[Values and types -> Vectors].
 
 a|
@@ -1780,7 +1780,7 @@ label:new[]
 * `"textembedding-gecko@003"`
 * `"textembedding-gecko-multilingual@001"`
 
-| Added support for additional xref:genai-integrations.adoc#ai-providers[Vertex AI] vector encoding models.
+| Added support for more Vertex AI vector encoding models in link:https://neo4j.com/docs/genai/plugin/current/[GenAI plugin].
 Also added support for Vertex AI `taskType` and `title` embedding parameters.
 |===
 

diff --git a/modules/ROOT/pages/functions/index.adoc b/modules/ROOT/pages/functions/index.adoc
@@ -88,17 +88,6 @@ Database functions provide information about databases.
 |===
 
 
-
-[[header-query-functions-genai]]
-== GenAI functions
-
-[options="header"]
-|===
-| Function | Signature | Description
-1.1+| xref:genai-integrations.adoc#single-embedding[`genai.vector.encode()`]  | `genai.vector.encode(resource :: STRING, provider :: STRING, configuration :: MAP = {}) :: LIST<FLOAT>` | Encode a given resource as a vector using the named provider.
-|===
-
-
 [[header-query-functions-graph]]
 == Graph functions
 

diff --git a/modules/ROOT/pages/genai-integrations.adoc b/modules/ROOT/pages/genai-integrations.adoc
diff --git a/modules/ROOT/pages/indexes/semantic-indexes/vector-indexes.adoc b/modules/ROOT/pages/indexes/semantic-indexes/vector-indexes.adoc
@@ -47,7 +47,7 @@ As of Neo4j 2025.10, they can also be more efficiently stored as xref:values-and
 ====
 For information about how embeddings can be generated and stored as properties, see:
 
-* xref:genai-integrations.adoc[]
+* link:https://neo4j.com/docs/genai/plugin/current/embeddings/[GenAI documentation -> Create and store embeddings in a Neo4j database]
 * link:https://neo4j.com/docs/genai/tutorials/embeddings-vector-indexes/[GenAI documentation -> Embeddings & Vector Indexes Tutorial]
 ====
 

diff --git a/modules/ROOT/pages/values-and-types/vector.adoc b/modules/ROOT/pages/values-and-types/vector.adoc
@@ -2,7 +2,7 @@
 :description: Create and store vectors (embeddings) as properties on nodes and relationships, and use them for efficient semantic retrieval with vector indexes and the GenAI plugin.
 :page-role: new-neo4j-2025.10
 
-`VECTOR` values can be created and stored as xref:indexes/semantic-indexes/vector-indexes.adoc#embeddings[embedding] properties on nodes and relationships, and used for efficient semantic retrieval using xref:indexes/semantic-indexes/vector-indexes.adoc[vector indexes] and the xref:genai-integrations.adoc[GenAI plugin].
+`VECTOR` values can be created and stored as xref:indexes/semantic-indexes/vector-indexes.adoc#embeddings[embedding] properties on nodes and relationships, and used for efficient semantic retrieval using xref:indexes/semantic-indexes/vector-indexes.adoc[vector indexes] and the link:https://neo4j.com/docs/genai/plugin/current/[GenAI plugin].
 `VECTOR` values can also be measured and compared (in terms of similarity, distance, and norm) using xref:functions/vector.adoc[vector functions].
 
 
@@ -265,7 +265,4 @@ Moreover, reducing a vector's coordinate type (e.g., from `INTEGER16` to `INTEGE
 [TIP]
 You can convert vectors into lists via the functions xref:functions/list.adoc#functions-tointegerlist[`toIntegerList()`] and xref:functions/list.adoc#functions-tofloatlist[`toFloatList()`].
 
-For information about how to store embeddings as `VECTOR` values with the xref:genai-integrations.adoc[GenAI plugin], see:
-
-* xref:genai-integrations.adoc#single-embedding[Generate a single embedding and store it]
-* xref:genai-integrations.adoc#multiple-embeddings[Generate multiple embeddings and store them]
+For information about how to store embeddings as `VECTOR` values with the GenAI plugin, see link:https://neo4j.com/docs/genai/plugin/current/embeddings/[GenAI documentation -> Create and store embeddings in a Neo4j database].