diff --git a/cognee/infrastructure/InfrastructureConfig.py b/cognee/infrastructure/InfrastructureConfig.py
index aeff8c78c..fff2834ce 100644
--- a/cognee/infrastructure/InfrastructureConfig.py
+++ b/cognee/infrastructure/InfrastructureConfig.py
@@ -24,7 +24,7 @@ class InfrastructureConfig():
             self.vector_engine = WeaviateAdapter(
                 config.weaviate_url,
                 config.weaviate_api_key,
-                config.openai_key
+                embedding_engine = DefaultEmbeddingEngine()
             )
 
         return {
diff --git a/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py b/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py
index a299aac8a..4119c4361 100644
--- a/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py
+++ b/cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py
@@ -9,6 +9,7 @@ from ..models.DataPoint import DataPoint
 from ..models.ScoredResult import ScoredResult
 from ..embeddings.EmbeddingEngine import EmbeddingEngine
 
+
 class WeaviateAdapter(VectorDBInterface):
     async_pool: Pool = None
     embedding_engine: EmbeddingEngine = None
@@ -17,12 +18,12 @@ class WeaviateAdapter(VectorDBInterface):
         self.embedding_engine = embedding_engine
 
         self.client = weaviate.connect_to_wcs(
-            cluster_url = url,
-            auth_credentials = weaviate.auth.AuthApiKey(api_key),
+            cluster_url=url,
+            auth_credentials=weaviate.auth.AuthApiKey(api_key),
             # headers = {
             #     "X-OpenAI-Api-Key": openai_api_key
             # },
-            additional_config = wvc.init.AdditionalConfig(timeout = wvc.init.Timeout(init = 30))
+            additional_config=wvc.init.AdditionalConfig(timeout=wvc.init.Timeout(init=30))
         )
 
     async def embed_data(self, data: List[str]) -> List[float]:
@@ -30,12 +31,12 @@ class WeaviateAdapter(VectorDBInterface):
 
     async def create_collection(self, collection_name: str):
         return self.client.collections.create(
-            name = collection_name,
-            properties = [
+            name=collection_name,
+            properties=[
                 wvcc.Property(
-                    name = "text",
-                    data_type = wvcc.DataType.TEXT,
-                    skip_vectorization = True
+                    name="text",
+                    data_type=wvcc.DataType.TEXT,
+                    skip_vectorization=True
                 )
             ]
         )
@@ -44,13 +45,14 @@ class WeaviateAdapter(VectorDBInterface):
         return self.client.collections.get(collection_name)
 
     async def create_data_points(self, collection_name: str, data_points: List[DataPoint]):
-        data_vectors = await self.embed_data(list(map(lambda data_point: data_point.get_embeddable_data(), data_points)))
+        data_vectors = await self.embed_data(
+            list(map(lambda data_point: data_point.get_embeddable_data(), data_points)))
 
         def convert_to_weaviate_data_points(data_point: DataPoint):
             return DataObject(
-                uuid = data_point.id,
-                properties = data_point.payload,
-                vector = data_vectors[data_points.index(data_point)]
+                uuid=data_point.id,
+                properties=data_point.payload,
+                vector=data_vectors[data_points.index(data_point)]
             )
 
         objects = list(map(convert_to_weaviate_data_points, data_points))
@@ -58,35 +60,35 @@ class WeaviateAdapter(VectorDBInterface):
         return self.get_collection(collection_name).data.insert_many(objects)
 
     async def search(
-        self,
-        collection_name: str,
-        query_text: Optional[str] = None,
-        query_vector: Optional[List[float]] = None,
-        limit: int = None,
-        with_vector: bool = False
+            self,
+            collection_name: str,
+            query_text: Optional[str] = None,
+            query_vector: Optional[List[float]] = None,
+            limit: int = None,
+            with_vector: bool = False
     ):
         if query_text is None and query_vector is None:
             raise ValueError("One of query_text or query_vector must be provided!")
-    
+
         search_result = self.get_collection(collection_name).query.hybrid(
-            query = None,
-            vector = query_vector if query_vector is not None else (await self.embed_data([query_text]))[0],
-            limit = limit,
-            include_vector = with_vector,
-            return_metadata = wvc.query.MetadataQuery(score = True),
+            query=None,
+            vector=query_vector if query_vector is not None else (await self.embed_data([query_text]))[0],
+            limit=limit,
+            include_vector=with_vector,
+            return_metadata=wvc.query.MetadataQuery(score=True),
         )
 
         return list(map(lambda result: ScoredResult(
-            id = str(result.uuid),
-            payload = result.properties,
-            score = float(result.metadata.score)
+            id=str(result.uuid),
+            payload=result.properties,
+            score=float(result.metadata.score)
         ), search_result.objects))
 
-    async def batch_search(self, collection_name: str, query_texts: List[str], limit: int,  with_vectors: bool = False):
+    async def batch_search(self, collection_name: str, query_texts: List[str], limit: int, with_vectors: bool = False):
         def query_search(query_vector):
-            return self.search(collection_name, query_vector = query_vector, limit = limit, with_vector = with_vectors)
+            return self.search(collection_name, query_vector=query_vector, limit=limit, with_vector=with_vectors)
 
         return [await query_search(query_vector) for query_vector in await self.embed_data(query_texts)]
 
     async def prune(self):
-        self.client.collections.delete_all()
+        self.client.collections.delete_all()
\ No newline at end of file