diff --git a/sage/reranker.py b/sage/reranker.py
index db1e008..0aa2c37 100644
--- a/sage/reranker.py
+++ b/sage/reranker.py
@@ -28,14 +28,14 @@ def build_reranker(provider: str, model: Optional[str] = None, top_k: int = 5) -
         RerankerProvider.COHERE.value: "COHERE_API_KEY",
         RerankerProvider.NVIDIA.value: "NVIDIA_API_KEY",
         RerankerProvider.JINA.value: "JINA_API_KEY",
-        RerankerProvider.VOYAGE.value: "VOYAGE_API_KEY"
+        RerankerProvider.VOYAGE.value: "VOYAGE_API_KEY",
     }
 
     provider_defaults = {
         RerankerProvider.HUGGINGFACE.value: "cross-encoder/ms-marco-MiniLM-L-6-v2",
         RerankerProvider.COHERE.value: "rerank-english-v3.0",
         RerankerProvider.NVIDIA.value: "nvidia/nv-rerankqa-mistral-4b-v3",
-        RerankerProvider.VOYAGE.value: "rerank-1"
+        RerankerProvider.VOYAGE.value: "rerank-1",
     }
 
     model = model or provider_defaults.get(provider)
diff --git a/sage/vector_store.py b/sage/vector_store.py
index d9f69e0..826980e 100644
--- a/sage/vector_store.py
+++ b/sage/vector_store.py
@@ -8,8 +8,8 @@
 
 import marqo
 import nltk
-from langchain_community.retrievers import BM25Retriever
 from langchain.retrievers import EnsembleRetriever
+from langchain_community.retrievers import BM25Retriever
 from langchain_community.vectorstores import Marqo
 from langchain_community.vectorstores import Pinecone as LangChainPinecone
 from langchain_core.documents import Document
@@ -134,25 +134,28 @@ def upsert_batch(self, vectors: List[Vector], namespace: str):
         self.index.upsert(vectors=pinecone_vectors, namespace=namespace)
 
     def as_retriever(self, top_k: int, embeddings: Embeddings, namespace: str):
-        bm25_retriever = BM25Retriever(
-            embeddings=embeddings,
-            sparse_encoder=self.bm25_encoder,
-            index=self.index,
-            namespace=namespace,
-            top_k=top_k,
-        ) if self.bm25_encoder else None
+        bm25_retriever = (
+            BM25Retriever(
+                embeddings=embeddings,
+                sparse_encoder=self.bm25_encoder,
+                index=self.index,
+                namespace=namespace,
+                top_k=top_k,
+            )
+            if self.bm25_encoder
+            else None
+        )
 
         dense_retriever = LangChainPinecone.from_existing_index(
             index_name=self.index_name, embedding=embeddings, namespace=namespace
         ).as_retriever(search_kwargs={"k": top_k})
-        
+
         if bm25_retriever:
-            return EnsembleRetriever(retrievers=[dense_retriever, bm25_retriever], weights=[self.alpha, 1-self.alpha])
+            return EnsembleRetriever(retrievers=[dense_retriever, bm25_retriever], weights=[self.alpha, 1 - self.alpha])
         else:
             return dense_retriever
 
 
-
 class MarqoVectorStore(VectorStore):
     """Vector store implementation using Marqo."""