Fix embedding retrieval top-k limit

integrations/astra/src/haystack_integrations/document_stores/astra/astra_client.py

@@ -202,7 +202,7 @@ def _format_query_response(responses, include_metadata, include_values):
         return QueryResponse(final_res)
 
     def _query(self, vector, top_k, filters=None):
-        query = {"sort": {"$vector": vector}, "options": {"limit": top_k, "includeSimilarity": True}}
+        query = {"sort": {"$vector": vector}, "limit": top_k, "includeSimilarity": True}
 
         if filters is not None:
             query["filter"] = filters
@@ -222,6 +222,7 @@ def find_documents(self, find_query):
             filter=find_query.get("filter"),
             sort=find_query.get("sort"),
             limit=find_query.get("limit"),
+            include_similarity=find_query.get("includeSimilarity"),
             projection={"*": 1},
         )
 

integrations/astra/tests/test_embedding_retrieval.py

@@ -0,0 +1,48 @@
+import os
+
+import pytest
+from haystack import Document
+from haystack.document_stores.types import DuplicatePolicy
+
+from haystack_integrations.document_stores.astra import AstraDocumentStore
+
+
+@pytest.mark.integration
+@pytest.mark.skipif(
+    os.environ.get("ASTRA_DB_APPLICATION_TOKEN", "") == "", reason="ASTRA_DB_APPLICATION_TOKEN env var not set"
+)
+@pytest.mark.skipif(os.environ.get("ASTRA_DB_API_ENDPOINT", "") == "", reason="ASTRA_DB_API_ENDPOINT env var not set")
+class TestEmbeddingRetrieval:
+
+    @pytest.fixture
+    def document_store(self) -> AstraDocumentStore:
+        return AstraDocumentStore(
+            collection_name="haystack_integration",
+            duplicates_policy=DuplicatePolicy.OVERWRITE,
+            embedding_dimension=768,
+        )
+
+    @pytest.fixture(autouse=True)
+    def run_before_and_after_tests(self, document_store: AstraDocumentStore):
+        """
+        Cleaning up document store
+        """
+        document_store.delete_documents(delete_all=True)
+        assert document_store.count_documents() == 0
+
+    def test_search_with_top_k(self, document_store):
+        query_embedding = [0.1] * 768
+        common_embedding = [0.8] * 768
+
+        documents = [Document(content=f"This is document number {i}", embedding=common_embedding) for i in range(0, 3)]
+
+        document_store.write_documents(documents)
+
+        top_k = 2
+
+        result = document_store.search(query_embedding, top_k)
+
+        assert top_k == len(result)
+
+        for document in result:
+            assert document.score is not None