diff --git a/qdrant-landing/content/articles/vector-search-filtering.md b/qdrant-landing/content/articles/vector-search-filtering.md index 043895ba8..4cf7ed6bf 100644 --- a/qdrant-landing/content/articles/vector-search-filtering.md +++ b/qdrant-landing/content/articles/vector-search-filtering.md @@ -14,7 +14,7 @@ Imagine you sell computer hardware. To help shoppers easily find products on you ![vector-search-ecommerce](/articles_data/vector-search-filtering/vector-search-ecommerce.png) - If you’re selling computers and have extensive data on laptops, desktops, and accessories, your search feature should guide customers to the exact device they want - or a **very similar** match needed. + If you’re selling computers and have extensive data on laptops, desktops, and accessories, your search feature should guide customers to the exact device they want - or at least a **very similar** match. When storing data in Qdrant, each product is a point, consisting of an `id`, a `vector` and `payload`: @@ -748,4 +748,4 @@ The easiest way to reach that "Hello World" moment is to [**try filtering in a l **It's all in your free cluster!** -[![qdrant-hybrid-cloud](/docs/homepage/cloud-cta.png)](https://qdrant.to/cloud) \ No newline at end of file +[![qdrant-hybrid-cloud](/docs/homepage/cloud-cta.png)](https://qdrant.to/cloud) diff --git a/qdrant-landing/content/documentation/concepts/points.md b/qdrant-landing/content/documentation/concepts/points.md index 266823c0e..d9d53382c 100755 --- a/qdrant-landing/content/documentation/concepts/points.md +++ b/qdrant-landing/content/documentation/concepts/points.md @@ -338,10 +338,10 @@ Here is a list of supported vector types: |-|-| | Dense Vectors | A regular vectors, generated by majority of the embedding models. | | Sparse Vectors | Vectors with no fixed length, but only a few non-zero elements.
Useful for exact token match and collaborative filtering recommendations. | -| MultiVectors | Matrices of numbers with fixed length but variable height.
Usually obtained from late interraction models like ColBERT. | +| MultiVectors | Matrices of numbers with fixed length but variable height.
Usually obtained from late interaction models like ColBERT. | It is possible to attach more than one type of vector to a single point. -In Qdrant we call it Named Vectors. +In Qdrant we call these Named Vectors. Read more about vector types, how they are stored and optimized in the [vectors](/documentation/concepts/vectors/) section. diff --git a/qdrant-landing/content/documentation/guides/installation.md b/qdrant-landing/content/documentation/guides/installation.md index fd89a85e0..1358197d1 100644 --- a/qdrant-landing/content/documentation/guides/installation.md +++ b/qdrant-landing/content/documentation/guides/installation.md @@ -64,7 +64,7 @@ For production, you can use our Qdrant Cloud to run Qdrant either fully managed For testing or development setups, you can run the Qdrant container or as a binary executable. -If you want to run Qdrant in your own infrastructure, without any cloud connection, we recommend to install Qdrant in a Kubernetes cluster with our Helm chart, or to use our Qdrant Enterprise Operator +If you want to run Qdrant in your own infrastructure, without any cloud connection, we recommend to install Qdrant in a Kubernetes cluster with our Helm chart, or to use our Qdrant Enterprise Operator. ## Production