Update documentation

fix invalid domain type reference and make sure to include required arguemtns

Author: christophstrobl

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

@@ -6,13 +6,13 @@ Annotated search methods use the `@VectorSearch` annotation to define parameters
 ----
 interface CommentRepository extends Repository<Comment, String> {
 
-  @VectorSearch(indexName = "cos-index", filter = "{country: ?0}")
-  SearchResults<WithVector> searchAnnotatedByCountryAndEmbeddingWithin(String country, Vector embedding,
+  @VectorSearch(indexName = "cos-index", filter = "{country: ?0}", limit=100)
+  SearchResults<Comment> searchAnnotatedByCountryAndEmbeddingWithin(String country, Vector embedding,
       Score distance);
 
-  @VectorSearch(indexName = "my-index", filter = "{country: ?0}", numCandidates = "#{#limit * 20}",
+  @VectorSearch(indexName = "my-index", filter = "{country: ?0}", limit="?3", numCandidates = "#{#limit * 20}",
 				searchType = VectorSearchOperation.SearchType.ANN)
-  List<WithVector> findAnnotatedByCountryAndEmbeddingWithin(String country, Vector embedding, Score distance, int limit);
+  List<Comment> findAnnotatedByCountryAndEmbeddingWithin(String country, Vector embedding, Score distance, int limit);
 }
 ----
 ====

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

@@ -7,13 +7,13 @@ MongoDB Search methods must use the `@VectorSearch` annotation to define the ind
 interface CommentRepository extends Repository<Comment, String> {
 
   @VectorSearch(indexName = "my-index")
-  SearchResults<Comment> searchByEmbeddingNear(Vector vector, Score score);
+  SearchResults<Comment> searchTop10ByEmbeddingNear(Vector vector, Score score);
 
   @VectorSearch(indexName = "my-index")
-  SearchResults<Comment> searchByEmbeddingWithin(Vector vector, Range<Similarity> range);
+  SearchResults<Comment> searchTop10ByEmbeddingWithin(Vector vector, Range<Similarity> range);
 
   @VectorSearch(indexName = "my-index")
-  SearchResults<Comment> searchByCountryAndEmbeddingWithin(String country, Vector vector, Range<Similarity> range);
+  SearchResults<Comment> searchTop10ByCountryAndEmbeddingWithin(String country, Vector vector, Range<Similarity> range);
 }
 ----
 ====

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

@@ -8,12 +8,18 @@ interface CommentRepository extends Repository<Comment, String> {
   SearchResults<Comment> searchByCountryAndEmbeddingNear(String country, Vector vector, Score score,
     Limit limit);
 
-  @VectorSearch(indexName = "my-index")
-  SearchResults<WithVector> searchAnnotatedByCountryAndEmbeddingWithin(String country, Vector embedding,
+  @VectorSearch(indexName = "my-index", limit=10)
+  SearchResults<Comment> searchAnnotatedByCountryAndEmbeddingWithin(String country, Vector embedding,
       Score score);
 
 }
 
 SearchResults<Comment> results = repository.searchByCountryAndEmbeddingNear("en", Vector.of(…), Score.of(0.9), Limit.of(10));
 ----
 ====
+
+[TIP]
+====
+The MongoDB https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/[vector search aggregation] stage defines a set of required arguments and restrictions.
+Please make sure to follow the guidelines and make sure to provide required arguments like `limit`.
+====

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

@@ -9,13 +9,13 @@ The scoring function defaults to `ScoringFunction.unspecified()` as there is no
 interface CommentRepository extends Repository<Comment, String> {
 
   @VectorSearch(…)
-  SearchResults<Comment> searchByEmbeddingNear(Vector vector, Score similarity);
+  SearchResults<Comment> searchTop10ByEmbeddingNear(Vector vector, Score similarity);
 
   @VectorSearch(…)
-  SearchResults<Comment> searchByEmbeddingNear(Vector vector, Similarity similarity);
+  SearchResults<Comment> searchTop10ByEmbeddingNear(Vector vector, Similarity similarity);
 
   @VectorSearch(…)
-  SearchResults<Comment> searchByEmbeddingNear(Vector vector, Range<Similarity> range);
+  SearchResults<Comment> searchTop10ByEmbeddingNear(Vector vector, Range<Similarity> range);
 }
 
 repository.searchByEmbeddingNear(Vector.of(…), Score.of(0.9));                <1>