style: format s3 vectors transformation and rag endpoints

litellm/llms/s3_vectors/vector_stores/transformation.py

@@ -68,7 +68,9 @@ class S3VectorsVectorStoreConfig(BaseVectorStoreConfig, BaseAWSLLM):
         """Return the router iff it serves ``embedding_model`` as a deployment."""
         if router is None:
             return None
-        model_list: Final = [dict(m) for m in (router.get_model_list() or ())]  # mutable-ok: resolve_embedding_router requires list[dict]
+        model_list: Final = [
+            dict(m) for m in (router.get_model_list() or ())
+        ]  # mutable-ok: resolve_embedding_router requires list[dict]
         return resolve_embedding_router(embedding_model=embedding_model, llm_router=router, llm_model_list=model_list)
 
     def transform_search_vector_store_request(

litellm/proxy/rag_endpoints/endpoints.py

@@ -717,7 +717,10 @@ async def rag_query(
             vector_store_id=retrieval_config["vector_store_id"],
             user_api_key_dict=user_api_key_dict,
         )
-        merged_retrieval_config: Final = {**store_data, **retrieval_config}  # mutable-ok: litellm.aquery requires a plain dict payload
+        merged_retrieval_config: Final = {
+            **store_data,
+            **retrieval_config,
+        }  # mutable-ok: litellm.aquery requires a plain dict payload
 
         # Add litellm data
         request_data: dict[str, object] = {}