diff --git a/docs/v0x/components/vectordbs/dbs/opensearch.mdx b/docs/v0x/components/vectordbs/dbs/opensearch.mdx
index 4c0a729022..6eab4e127a 100644
--- a/docs/v0x/components/vectordbs/dbs/opensearch.mdx
+++ b/docs/v0x/components/vectordbs/dbs/opensearch.mdx
@@ -42,12 +42,19 @@ config = {
             "host": "your-domain.us-west-2.aoss.amazonaws.com",
             "port": 443,
             "http_auth": auth,
-            "embedding_model_dims": 1024,
+            "embedding_model_dims": 1536,  # should match embedder's dimensions 
             "connection_class": RequestsHttpConnection,
             "pool_maxsize": 20,
             "use_ssl": True,
             "verify_certs": True
         }
+    },
+    "embedder": {
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-small",
+            "embedding_dims": 1536
+        }
     }
 }
 ```
diff --git a/mem0/utils/factory.py b/mem0/utils/factory.py
index ab3fc77a3c..b9bfe70415 100644
--- a/mem0/utils/factory.py
+++ b/mem0/utils/factory.py
@@ -192,6 +192,10 @@ class VectorStoreFactory:
     def create(cls, provider_name, config):
         class_type = cls.provider_to_class.get(provider_name)
         if class_type:
+            # Ensure the config includes embedding_model_dims, using the default from VectorStoreBase if not defined
+            if "embedding_model_dims" not in config:
+                from mem0.vector_stores.base import VectorStoreBase
+                config["embedding_model_dims"] = VectorStoreBase.embedding_model_dims
             if not isinstance(config, dict):
                 config = config.model_dump()
             vector_store_instance = load_class(class_type)
diff --git a/mem0/vector_stores/base.py b/mem0/vector_stores/base.py
index 3e22499d79..e025d6275b 100644
--- a/mem0/vector_stores/base.py
+++ b/mem0/vector_stores/base.py
@@ -2,6 +2,7 @@
 
 
 class VectorStoreBase(ABC):
+    embedding_model_dims = 1536  # Default value for embedding model dimensions which aligns with default OpenAI embedder dimensions 
     @abstractmethod
     def create_col(self, name, vector_size, distance):
         """Create a new collection."""