Documentation.

See #1573

src/main/antora/modules/ROOT/nav.adoc

@@ -28,6 +28,7 @@
 ** xref:repositories/create-instances.adoc[]
 ** xref:repositories/query-methods-details.adoc[]
 ** xref:cassandra/repositories/query-methods.adoc[]
+** xref:cassandra/repositories/vector-search.adoc[]
 ** xref:repositories/projections.adoc[]
 ** xref:repositories/custom-implementations.adoc[]
 ** xref:repositories/core-domain-events.adoc[]

src/main/antora/modules/ROOT/pages/cassandra/repositories/vector-search.adoc

@@ -0,0 +1,8 @@
+:vector-search-intro-include: data-cassandra::partial$vector-search-intro-include.adoc
+:vector-search-model-include: data-cassandra::partial$vector-search-model-include.adoc
+:vector-search-repository-include: data-cassandra::partial$vector-search-repository-include.adoc
+:vector-search-scoring-include: data-cassandra::partial$vector-search-scoring-include.adoc
+:vector-search-method-derived-include: data-cassandra::partial$vector-search-method-derived-include.adoc
+:vector-search-method-annotated-include: data-cassandra::partial$vector-search-method-annotated-include.adoc
+
+include::{commons}@data-commons::page$repositories/vector-search.adoc[]

src/main/antora/modules/ROOT/partials/vector-search-intro-include.adoc

@@ -0,0 +1 @@
+

src/main/antora/modules/ROOT/partials/vector-search-method-annotated-include.adoc

@@ -0,0 +1,19 @@
+.Using `@Query`
+====
+[source,java]
+----
+interface CommentRepository extends Repository<Comment, String> {
+
+  @Query("""
+         SELECT id, description, country, similarity_cosine(embedding,:embedding) AS score
+         FROM comments
+         ORDER BY embedding ANN OF :embedding LIMIT :limit
+         """)
+  SearchResults<WithVectorFields> searchAnnotatedByEmbeddingNear(Vector embedding, Limit limit);
+}
+----
+====
+
+Cassandra does not allow to limit search results by their score.
+Declared search methods can include a `score` projection column if you wish to return the score value.
+You can declare a `ScoringFunction` argument to specify which scoring function to use to calculate similarity.

src/main/antora/modules/ROOT/partials/vector-search-method-derived-include.adoc

@@ -0,0 +1,16 @@
+.Using `Near` and `Within` Keywords in Repository Search Methods
+====
+[source,java]
+----
+interface CommentRepository extends Repository<Comment, String> {
+
+  List<Comment> searchByEmbeddingNear(Vector vector);
+
+  SearchResults<Comment> searchByEmbeddingNear(Vector vector, ScoringFunction function);
+
+}
+----
+====
+
+Cassandra does not allow to limit search results by their score.
+You can declare a `ScoringFunction` argument to specify which scoring function to use to calculate similarity.

src/main/antora/modules/ROOT/partials/vector-search-model-include.adoc

@@ -0,0 +1,19 @@
+====
+[source,java]
+----
+class Comment {
+
+  @Id String id;
+  String country;
+  String comment;
+
+  @VectorType(dimensions = 5)
+  @SaiIndexed
+  Vector embedding;
+
+  // getters, setters, …
+}
+----
+====
+
+`@VectorType` and `@SaiIndexed` annotations are used to help with schema generation and type hints.

src/main/antora/modules/ROOT/partials/vector-search-repository-include.adoc

@@ -0,0 +1,13 @@
+.Using `SearchResults<T>` in a Repository Search Method
+====
+[source,java]
+----
+interface CommentRepository extends Repository<Comment, String> {
+
+  SearchResults<Comment> searchByEmbeddingNear(Vector vector, ScoringFunction function, Limit limit);
+
+}
+
+SearchResults<Comment> results = repository.searchByEmbeddingNear(Vector.of(…), ScoringFunction.cosine(), Limit.of(10));
+----
+====

src/main/antora/modules/ROOT/partials/vector-search-scoring-include.adoc

@@ -0,0 +1,20 @@
+Cassandra reports the score directly as similarity value through `similarity_euclidean`, `similarity_cosine`, and `similarity_dot_product` functions.
+
+.Using `ScoringFunction` in a Repository Search Methods
+====
+[source,java]
+----
+interface CommentRepository extends Repository<Comment, String> {
+
+  SearchResults<Comment> searchByEmbeddingNear(Vector vector, ScoringFunction function);
+}
+
+repository.searchByEmbeddingNear(Vector.of(…), ScoringFunction.cosine());     <1>
+
+repository.searchByEmbeddingNear(Vector.of(…), ScoringFunction.euclidean());  <2>
+----
+
+<1> Run a search and return results using the Cosine distance function to compute similarity.
+<2> Run a search and return results using the Euclidean distance function to compute similarity.
+====
+