diff --git a/docarray/array/storage/weaviate/find.py b/docarray/array/storage/weaviate/find.py
index a336a0d62a1..3a073dd5739 100644
--- a/docarray/array/storage/weaviate/find.py
+++ b/docarray/array/storage/weaviate/find.py
@@ -132,7 +132,7 @@ def _find(
            the closest Document objects for each of the queries in `query`.
 
         Note: Weaviate returns `certainty` values. To get cosine similarities one needs to use `cosine_sim = 2*certainty - 1` as explained here:
-                  https://www.semi.technology/developers/weaviate/current/more-resources/faq.html#q-how-do-i-get-the-cosine-similarity-from-weaviates-certainty
+                  https://weaviate.io/developers/weaviate/current/more-resources/faq.html#q-how-do-i-get-the-cosine-similarity-from-weaviates-certainty
         """
 
         num_rows, _ = ndarray.get_array_rows(query)
diff --git a/docs/advanced/document-store/weaviate.md b/docs/advanced/document-store/weaviate.md
index 527001bae8e..fe3f2d97e92 100644
--- a/docs/advanced/document-store/weaviate.md
+++ b/docs/advanced/document-store/weaviate.md
@@ -1,7 +1,7 @@
 (weaviate)=
 # Weaviate
 
-One can use [Weaviate](https://www.semi.technology) as the document store for DocumentArray. It is useful when one wants to have faster Document retrieval on embeddings, i.e. `.match()`, `.find()`.
+One can use [Weaviate](https://weaviate.io) as the document store for DocumentArray. It is useful when one wants to have faster Document retrieval on embeddings, i.e. `.match()`, `.find()`.
 
 ````{tip}
 This feature requires `weaviate-client`. You can install it via `pip install "docarray[weaviate]".`