Apply suggestions from code review

Co-authored-by: bogdankostic <bogdankostic@web.de>

Author: julian-risch

integrations/opensearch/src/haystack_integrations/document_stores/opensearch/document_store.py

@@ -1515,7 +1515,7 @@ async def _embedding_retrieval_async(
         search_kwargs: dict[str, Any] | None = None,
     ) -> list[Document]:
         """
-        Asynchronously retrieve documents most similar to the query embedding using a vector similarity metric.
+        Asynchronously retrieves documents most similar to the query embedding using a vector similarity metric.
 
         It uses the OpenSearch's Approximate k-Nearest Neighbors search algorithm.
 

integrations/qdrant/src/haystack_integrations/components/retrievers/qdrant/retriever.py

@@ -482,7 +482,7 @@ async def run_async(
 @component
 class QdrantHybridRetriever:
     """
-    A component for retrieving documents from an QdrantDocumentStore using both dense and sparse vectors.
+    A component for retrieving documents from a QdrantDocumentStore using both dense and sparse vectors.
 
     Fuses the results using Reciprocal Rank Fusion.