Fix CI issues related to missing dependency (#3096)

mem0/vector_stores/mongodb.py

@@ -1,5 +1,5 @@
 import logging
-from typing import List, Optional, Dict, Any, Callable
+from typing import List, Optional, Dict, Any
 
 from pydantic import BaseModel
 
@@ -26,13 +26,7 @@ class MongoDB(VectorStoreBase):
     VECTOR_TYPE = "knnVector"
     SIMILARITY_METRIC = "cosine"
 
-    def __init__(
-        self,
-        db_name: str,
-        collection_name: str,
-        embedding_model_dims: int,
-        mongo_uri: str
-    ):
+    def __init__(self, db_name: str, collection_name: str, embedding_model_dims: int, mongo_uri: str):
         """
         Initialize the MongoDB vector store with vector search capabilities.
 
@@ -46,9 +40,7 @@ class MongoDB(VectorStoreBase):
         self.embedding_model_dims = embedding_model_dims
         self.db_name = db_name
 
-        self.client = MongoClient(
-            mongo_uri
-        )
+        self.client = MongoClient(mongo_uri)
         self.db = self.client[db_name]
         self.collection = self.create_col()
 
@@ -119,7 +111,9 @@ class MongoDB(VectorStoreBase):
         except PyMongoError as e:
             logger.error(f"Error inserting data: {e}")
 
-    def search(self, query: str, query_vector: List[float], limit=5, filters: Optional[Dict] = None) -> List[OutputData]:
+    def search(
+        self, query: str, query_vector: List[float], limit=5, filters: Optional[Dict] = None
+    ) -> List[OutputData]:
         """
         Search for similar vectors using the vector search index.
 
@@ -285,7 +279,7 @@ class MongoDB(VectorStoreBase):
         except PyMongoError as e:
             logger.error(f"Error listing documents: {e}")
             return []
-    
+
     def reset(self):
         """Reset the index by deleting and recreating it."""
         logger.warning(f"Resetting index {self.collection_name}...")

mem0/vector_stores/opensearch.py

@@ -88,7 +88,7 @@ class OpenSearchDB(VectorStoreBase):
             self.client.indices.create(index=name, body=index_settings)
 
             # Wait for index to be ready
-            max_retries = 180 # 3 minutes timeout
+            max_retries = 180  # 3 minutes timeout
             retry_count = 0
             while retry_count < max_retries:
                 try: