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alaeddine-13 merged 3 commits into from
Jun 13, 2022
Merged

docs: weaviate doc fixes #402

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7c2e3f5
chore: set correct URL
bobvanluijt Jun 9, 2022
2ea37e0
chore: set correct URL
bobvanluijt Jun 9, 2022
553432f
docs: update link
alaeddine-13 Jun 10, 2022
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2 changes: 1 addition & 1 deletion docarray/array/storage/weaviate/find.py
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Expand Up @@ -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)
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2 changes: 1 addition & 1 deletion docs/advanced/document-store/weaviate.md
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@@ -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]".`
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