Apply suggestions from code review

Co-authored-by: Michael Webb <[email protected]>

Author: rsill-neo4j

modules/ROOT/pages/directives/indexes-and-constraints.adoc

@@ -269,7 +269,7 @@ This embedding is then compared to the node vector embeddings in the database.
 ==== 
 * The database must be Neo4j version 5.15 or higher.
 * The node vector embeddings already exist in the database. See link:https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/vector-indexes/[Vector indexes] to learn more about vector indexes in Cypher and Neo4j.
-* The embeddings must have been created using the same method.
+* The embeddings must have been created using the same method, that is, the same provider and model.
 * Queries by vector index cannot be performed across multiple labels.
 * Queries by phrase require credentials for the Neo4j GenAI plugin.
 ====
@@ -311,7 +311,7 @@ Allowed values for the `provider` field are defined by the available link:https:
 
 ==== Query by vector index
 
-Perform a nearest neighbor search by passing a vector index to find nodes with a vector embedding similar to that index.
+Perform a nearest neighbor search by passing a vector to find nodes with a vector embedding similar to that vector.
 
 .Type definition
 [source, graphql]
@@ -327,7 +327,7 @@ type Product @vector(indexes: [{
 }
 ----
 
-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. 
+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 embedding has been created for the `description` property of each node. 
 
 .Example query
 [source, graphql]
@@ -363,11 +363,11 @@ The input `$vector` is a list of `FLOAT` values and should look similar to this:
 }
 ----
 
-The query returns all `Product` nodes with a vector embedding on their `descriptionVector` property which is similar to the vector index argument `$vector`.
+The query returns all `Product` nodes with a vector embedding on their `descriptionVector` property which is similar to the query argument `$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.
+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 compare it to existing vector embeddings on nodes in the database.
 
 [NOTE]
 ====
@@ -389,7 +389,7 @@ type Product @vector(indexes: [{
 }
 ----
 
-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. 
+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 embedding has been created for the `description` property of each node. 
 
 .Example query
 [source, graphql]
@@ -412,4 +412,4 @@ query SearchProductsByPhrase($phrase: String!) {
 ----
 
 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.
+Then it returns all `Product` nodes with a vector embedding on their `descriptionVector` property which are similar to the vector embedding generated by the plugin.