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Fix Docs : minor grammar fixes (#2218)

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* fix(docs): fix typos and some links in documentation

* fix(docs): correct typo in filtering.md

* Update qdrant-landing/content/documentation/headless/snippets/inference/jinaai-upsert/generated/typescript.md

Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>

* Update qdrant-landing/content/documentation/headless/snippets/inference/multiple/generated/typescript.md

Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>

* Update qdrant-landing/content/documentation/hybrid-cloud/configure-scale-upgrade.md

Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>

* Update qdrant-landing/content/documentation/cloud-api.md

Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>

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Co-authored-by: Abdon Pijpelink <abdon.pijpelink@qdrant.com>
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Mohamed Arbi
2026-03-26 17:23:36 +01:00
committed by GitHub
co-authored by Abdon Pijpelink
parent 3f3ef1ad20
commit c0db45ed8f
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qdrant-landing/content/documentation/manage-data/vectors.md
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@@ -183,7 +183,7 @@ Similarly, you can use inference at query time by providing the text or image to
## Datatypes
Newest versions of embeddings models generate vectors with very large dimentionalities.
Newest versions of embeddings models generate vectors with very large dimensionalities.
With OpenAI's `text-embedding-3-large` embedding model, the dimensionality can go up to 3072.
The amount of memory required to store such vectors grows linearly with the dimensionality,