diff --git a/swarms/memory/pinecone.py b/swarms/memory/pinecone.py
index 6c2032b5..9065d661 100644
--- a/swarms/memory/pinecone.py
+++ b/swarms/memory/pinecone.py
@@ -1,12 +1,12 @@
 from typing import Optional
-from swarms.memory.base import BaseVector
+from swarms.memory.base import BaseVectorStore
 import pinecone
 from attr import define, field
 from swarms.utils.hash import str_to_hash
 
 
 @define
-class PineconeVectorStoreStore(BaseVector):
+class PineconeVectorStoreStore(BaseVectorStore):
     """
     PineconeVectorStore is a vector storage driver that uses Pinecone as the underlying storage engine.
 
@@ -24,11 +24,11 @@ class PineconeVectorStoreStore(BaseVector):
     Methods:
         upsert_vector(vector: list[float], vector_id: Optional[str] = None, namespace: Optional[str] = None, meta: Optional[dict] = None, **kwargs) -> str:
             Upserts a vector into the index.
-        load_entry(vector_id: str, namespace: Optional[str] = None) -> Optional[BaseVector.Entry]:
+        load_entry(vector_id: str, namespace: Optional[str] = None) -> Optional[BaseVectorStore.Entry]:
             Loads a single vector from the index.
-        load_entries(namespace: Optional[str] = None) -> list[BaseVector.Entry]:
+        load_entries(namespace: Optional[str] = None) -> list[BaseVectorStore.Entry]:
             Loads all vectors from the index.
-        query(query: str, count: Optional[int] = None, namespace: Optional[str] = None, include_vectors: bool = False, include_metadata=True, **kwargs) -> list[BaseVector.QueryResult]:
+        query(query: str, count: Optional[int] = None, namespace: Optional[str] = None, include_vectors: bool = False, include_metadata=True, **kwargs) -> list[BaseVectorStore.QueryResult]:
             Queries the index for vectors similar to the given query string.
         create_index(name: str, **kwargs) -> None:
             Creates a new index.
@@ -121,7 +121,7 @@ class PineconeVectorStoreStore(BaseVector):
 
     def load_entry(
         self, vector_id: str, namespace: Optional[str] = None
-    ) -> Optional[BaseVector.Entry]:
+    ) -> Optional[BaseVectorStore.Entry]:
         """Load entry"""
         result = self.index.fetch(
             ids=[vector_id], namespace=namespace
@@ -131,7 +131,7 @@ class PineconeVectorStoreStore(BaseVector):
         if len(vectors) > 0:
             vector = vectors[0]
 
-            return BaseVector.Entry(
+            return BaseVectorStore.Entry(
                 id=vector["id"],
                 meta=vector["metadata"],
                 vector=vector["values"],
@@ -142,7 +142,7 @@ class PineconeVectorStoreStore(BaseVector):
 
     def load_entries(
         self, namespace: Optional[str] = None
-    ) -> list[BaseVector.Entry]:
+    ) -> list[BaseVectorStore.Entry]:
         """Load entries"""
         # This is a hacky way to query up to 10,000 values from Pinecone. Waiting on an official API for fetching
         # all values from a namespace:
@@ -156,7 +156,7 @@ class PineconeVectorStoreStore(BaseVector):
         )
 
         return [
-            BaseVector.Entry(
+            BaseVectorStore.Entry(
                 id=r["id"],
                 vector=r["values"],
                 meta=r["metadata"],
@@ -174,12 +174,12 @@ class PineconeVectorStoreStore(BaseVector):
         # PineconeVectorStoreStorageDriver-specific params:
         include_metadata=True,
         **kwargs,
-    ) -> list[BaseVector.QueryResult]:
+    ) -> list[BaseVectorStore.QueryResult]:
         """Query vectors"""
         vector = self.embedding_driver.embed_string(query)
 
         params = {
-            "top_k": count if count else BaseVector.DEFAULT_QUERY_COUNT,
+            "top_k": count if count else BaseVectorStore.DEFAULT_QUERY_COUNT,
             "namespace": namespace,
             "include_values": include_vectors,
             "include_metadata": include_metadata,
@@ -188,7 +188,7 @@ class PineconeVectorStoreStore(BaseVector):
         results = self.index.query(vector, **params)
 
         return [
-            BaseVector.QueryResult(
+            BaseVectorStore.QueryResult(
                 id=r["id"],
                 vector=r["values"],
                 score=r["score"],