updates for @vector

Author: rsill-neo4j

modules/ROOT/content-nav.adoc

@@ -29,7 +29,6 @@
 *** xref:directives/schema-configuration/field-configuration.adoc[]
 ** xref:directives/indexes-and-constraints.adoc[]
 ** xref:directives/custom-logic.adoc[]
-** xref:directives/genai.adoc[]
 ** xref:directives/custom-directives.adoc[]
 
 * xref:queries-aggregations/index.adoc[Queries and aggregations]

modules/ROOT/pages/directives/genai.adoc

@@ -105,12 +105,15 @@ Particularly useful for types that are not correctly pluralized or are non-Engli
 |===
 | Directive | Description
 
-| xref::/directives/indexes-and-constraints.adoc#type-definitions-indexes-fulltext[`@fulltext`]
+| xref::/directives/indexes-and-constraints.adoc#_fulltext_indexes[`@fulltext`]
 | Indicates that there should be a fulltext index inserted into the database for the specified Node and its properties.
 
-| xref::/directives/indexes-and-constraints.adoc#type-definitions-constraints-unique[`@unique`]
+| xref::/directives/indexes-and-constraints.adoc#_unique_node_property_constraints[`@unique`]
 | Indicates that there should be a uniqueness constraint in the database for the fields that it is applied to.
 
+| xref::/directives/indexes-and-constraints.adoc#_vector_index_search[`@vector`]
+| Perform a vector index search on your database either based by passing in a vector index or a search phrase.
+
 |===
 
 == Custom logic
@@ -137,20 +140,6 @@ of any required fields that is passed as arguments to the custom resolver.
 
 |===
 
-== Generative AI
-
-[cols="2,5"]
-|===
-| Directive | Description
-
-| xref::/directives/genai.adoc#_vector[`@vector`]
-| Use an existing vector embedding on a node in the database to perform a nearest neighbor search.
-
-| xref::/directives/genai.adoc#_genai[`@genAI`]
-| Perform a query which utilizes the link::https://neo4j.com/docs/cypher-manual/current/genai-integrations/[Neo4j GenAI plugin] to create a vector embedding for a search phrase and then compares it to existing vector embeddings on nodes in the database.
-
-|===
-
 == OGM
 
 [cols="2,5"]

modules/ROOT/pages/directives/index.adoc

@@ -247,3 +247,149 @@ const schema = await neoSchema.getSchema();
 
 await neoSchema.assertIndexesAndConstraints({ options: { create: true }});
 ----
+
+
+:description: Directives related to generative AI in the Neo4j GraphQL Library.
+
+
+== Vector index search
+
+With the `@vector` GraphQL directive you can query your database to perform a vector index search.
+Queries are performed by passing in either a vector index or a query phrase.
+
+A query by vector index finds nodes with a vector embedding similar to that index, a nearest neighbor search.
+
+In contrast, a query by phrase (a string of text) forwards the phrase to the link:https://neo4j.com/docs/cypher-manual/current/genai-integrations/[Neo4j GenAI plugin] and the plugin generates a vector embedding for it.
+This embedding is then compared to the node vector embeddings in the database and the result set.
+
+[NOTE] 
+.Prerequisites
+==== 
+* The database must be Neo4j version 5.15 or higher.
+* The node vector embeddings already exist in the database.
+* The embeddings must have been created using the same method.
+* Queries by vector index cannot be performed across multiple labels.
+* Queries by phrase require credentials for the Neo4j GenAI plugin.
+====
+
+
+=== Definition
+
+[source, graphql]
+----
+"""Informs @neo4j/graphql that there should be a vector index in the database, allows users to search by the index in the generated schema."""
+directive @vector(indexes: [VectorIndexInput]!) on OBJECT
+----
+
+`VectorIndexInput` is defined as follows:
+
+[source, graphql]
+----
+input VectorIndexInput {
+  """(Required) The name of the vector index."""
+  indexName: String!
+  """(Required) The name of the embedding property on the node."""
+  property: String!
+  """(Required) The name of the query."""
+  queryName: String
+  """(Optional) The name of the provider."""
+  provider: String
+  """(Optional) The name of the callback function."""
+  callback: String
+}
+----
+
+If the optional field `provider` is set, the type can be used for a query by phrase, otherwise for a query by vector index. 
+
+=== Usage
+
+==== Query by vector index
+
+Perform a nearest neighbor search by passing a vector index to your database and find nodes with a vector embedding similar to that index.
+
+.Type definition
+[source, graphql]
+----
+type Product @vector(indexes: [{
+  indexName: "productDescriptionIndex",
+  property: "descriptionVector",
+  queryName: "searchByDescription"
+}]) {
+  id: ID!
+  name: String!
+  description: String!
+}
+----
+
+This defines the query to be performed on all `Product` nodes which have a vector index named `productDescriptionIndex` for the property `descriptionVector`, implying that a vector index has been created for the `description` property of each node. 
+
+.Example query
+[source, graphql]
+----
+query FindSimilarProducts($vector: [Float]!) {
+  searchByDescription(vector: $vector) {
+    productsConnection {
+      edges {
+        cursor
+        score
+        node {
+            id
+            name
+            description
+        }
+      }
+    }
+  }
+}
+----
+
+The query returns all `Product` nodes with a vector embedding on their `descriptionVector` property which is similar to the vector index argument `$vector`.
+
+=== Query by phrase
+
+Perform a query which utilizes the link:https://neo4j.com/docs/cypher-manual/current/genai-integrations/[Neo4j GenAI plugin] to create a vector embedding for a search phrase and then compares it to existing vector embeddings on nodes in the database.
+
+[NOTE]
+====
+Requires credentials for the plugin.
+====
+
+.Type definition
+[source, graphql]
+----
+type Product @vector(indexes: [{
+  indexName: "productDescriptionIndex",
+  property: "descriptionVector",
+  provider: OpenAI,  # Assuming this is configured in the server
+  queryName: "searchByPhrase"
+}]) {
+  id: ID!
+  name: String!
+  description: String!
+}
+----
+
+This defines the query to be performed on all `Product` nodes which have a vector index named `productDescriptionIndex` for the property `descriptionVector`, implying that a vector index has been created for the `description` property of each node. 
+
+.Example query
+[source, graphql]
+----
+query SearchProductsByPhrase($phrase: String!) {
+  searchByPhrase(phrase: $phrase) {
+    productsConnection {
+      edges {
+        cursor
+        score
+        node {
+            id
+            name
+            description
+        }
+      }
+    }
+  }
+}
+----
+
+First, the query passes the query phrase argument `$phrase` to the GenAI plugin and lets it generate a vector embedding for the phrase.
+Then it returns all `Product` nodes with a vector embedding on their `descriptionVector` property which is similar to the vector embedding generated by the plugin.