diff --git a/langchain_weaviate/vectorstores.py b/langchain_weaviate/vectorstores.py
index 42244b0..5706ece 100644
--- a/langchain_weaviate/vectorstores.py
+++ b/langchain_weaviate/vectorstores.py
@@ -241,9 +241,14 @@ def _perform_search(
         ):
             kwargs["return_properties"].append(self._text_key)
 
-        # workaround to handle test_max_marginal_relevance_search
         vector = kwargs.pop("vector", None)
-        if vector is None and query is not None:
+        if vector is None and query is None:
+            # raise an error because weaviate will do a fetch object query
+            # if both query and vector are None
+            raise ValueError("Either query or vector must be provided.")
+
+        # workaround to handle test_max_marginal_relevance_search
+        if vector is None:
             vector = self._embedding.embed_query(query)
 
         return_uuids = kwargs.pop("return_uuids", False)
diff --git a/tests/integration_tests/test_vectorstores.py b/tests/integration_tests/test_vectorstores.py
index baeb7a5..c1aaba9 100644
--- a/tests/integration_tests/test_vectorstores.py
+++ b/tests/integration_tests/test_vectorstores.py
@@ -802,3 +802,25 @@ def run_similarity_test(search_method):
     run_similarity_test("similarity_search")
 
     run_similarity_test("similarity_search_with_score")
+
+
+def test_invalid_search_param(weaviate_client, embedding_openai):
+    index_name = f"TestIndex_{uuid.uuid4().hex}"
+    text_key = "page"
+    weaviate_vector_store = WeaviateVectorStore(
+        weaviate_client, index_name, text_key, embedding_openai
+    )
+
+    with pytest.raises(ValueError) as excinfo:
+        weaviate_vector_store._perform_search(query=None, k=5)
+
+    assert str(excinfo.value) == "Either query or vector must be provided."
+
+    with pytest.raises(ValueError) as excinfo:
+        weaviate_vector_store._perform_search(query=None, vector=None, k=5)
+
+    assert str(excinfo.value) == "Either query or vector must be provided."
+
+    weaviate_vector_store._perform_search(query="hello", vector=None, k=5)
+
+    weaviate_vector_store._perform_search(query=None, vector=[1, 2, 3], k=5)