diff --git a/docarray/math/distance/torch.py b/docarray/math/distance/torch.py
index 84c1add5a94..cdd27b9e9d2 100644
--- a/docarray/math/distance/torch.py
+++ b/docarray/math/distance/torch.py
@@ -18,9 +18,8 @@ def cosine(
     :param device: the computational device for `embed_model`, can be either `cpu` or `cuda`.
     :return: np.ndarray  with ndim=2
     """
-    if device == 'cuda':
-        x_mat = x_mat.cuda()
-        y_mat = y_mat.cuda()
+    x_mat = x_mat.to(device)
+    y_mat = y_mat.to(device)
 
     a_n, b_n = x_mat.norm(dim=1)[:, None], y_mat.norm(dim=1)[:, None]
     a_norm = x_mat / torch.clamp(a_n, min=eps)
@@ -37,9 +36,8 @@ def euclidean(x_mat: 'tensor', y_mat: 'tensor', device: str = 'cpu') -> 'numpy.n
     :param device: the computational device for `embed_model`, can be either `cpu` or `cuda`.
     :return: np.ndarray  with ndim=2
     """
-    if device == 'cuda':
-        x_mat = x_mat.cuda()
-        y_mat = y_mat.cuda()
+    x_mat = x_mat.to(device)
+    y_mat = y_mat.to(device)
 
     return torch.cdist(x_mat, y_mat).cpu().detach().numpy()
 
@@ -54,8 +52,7 @@ def sqeuclidean(
     :param device: the computational device for `embed_model`, can be either `cpu` or `cuda`.
     :return: np.ndarray  with ndim=2
     """
-    if device == 'cuda':
-        x_mat = x_mat.cuda()
-        y_mat = y_mat.cuda()
+    x_mat = x_mat.to(device)
+    y_mat = y_mat.to(device)
 
     return (torch.cdist(x_mat, y_mat) ** 2).cpu().detach().numpy()
diff --git a/docs/advanced/document-store/index.md b/docs/advanced/document-store/index.md
index 74a02bd5665..19ec7d84f6e 100644
--- a/docs/advanced/document-store/index.md
+++ b/docs/advanced/document-store/index.md
@@ -157,9 +157,9 @@ DocArray supports multiple storage backends with different search features. The
 | [`Sqlite`](./sqlite.md)              | `DocumentArray(storage='sqlite')`        | ❌             | ❌                      | ✅             | 
 | [`Weaviate`](./weaviate.md)          | `DocumentArray(storage='weaviate')`      | ✅             | ✅                      | ✅             |
 | [`Qdrant`](./qdrant.md)              | `DocumentArray(storage='qdrant')`        | ✅             | ✅                      | ❌             |
-| [`Annlite`](./annlite.md)            | `DocumentArray(storage='annlite')`        | ✅             | ✅                      | ✅             |
+| [`Annlite`](./annlite.md)            | `DocumentArray(storage='annlite')`       | ✅             | ✅                      | ✅             |
 | [`ElasticSearch`](./elasticsearch.md) | `DocumentArray(storage='elasticsearch')` | ✅             | ✅                      | ✅             |
-| [`Redis`](./redis.md)                | `DocumentArray(storage='elasticsearch')` | ✅             | ✅                      | ✅             |
+| [`Redis`](./redis.md)                | `DocumentArray(storage='redis')`         | ✅             | ✅                      | ✅             |
 
 
 Here we understand by