Lint

Author: mpangrazzi

integrations/opensearch/tests/conftest.py

@@ -17,7 +17,7 @@ def _get_unique_index_name() -> str:
 
 
 @pytest.fixture
-def document_store(request):
+def document_store():
     """
     We use this document store for basic tests and for testing filters.
     `return_embedding` is set to True because in filters tests we compare embeddings.
@@ -45,7 +45,7 @@ def document_store(request):
 
 
 @pytest.fixture
-def document_store_2(request):
+def document_store_2():
     hosts = ["https://localhost:9200"]
     index = f"test_index_2_{_get_unique_index_name()}"
 
@@ -70,7 +70,7 @@ def document_store_2(request):
 
 
 @pytest.fixture
-def document_store_readonly(request):
+def document_store_readonly():
     """
     A document store that does not automatically create the underlying index.
     """
@@ -99,7 +99,7 @@ def document_store_readonly(request):
 
 
 @pytest.fixture
-def document_store_embedding_dim_4_no_emb_returned(request):
+def document_store_embedding_dim_4_no_emb_returned():
     """
     A document store with embedding dimension 4 that does not return embeddings.
     """
@@ -121,7 +121,7 @@ def document_store_embedding_dim_4_no_emb_returned(request):
 
 
 @pytest.fixture
-def document_store_embedding_dim_4_no_emb_returned_faiss(request):
+def document_store_embedding_dim_4_no_emb_returned_faiss():
     """
     A document store with embedding dimension 4 that uses a FAISS engine with HNSW algorithm for vector search.
     We use this document store for testing efficient k-NN filtering according to