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Merge pull request #1369 from breezykermo/breezykermo/fix-typos

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A few typo and grammar fixes
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Tim Visée
2025-01-06 11:08:24 +01:00
committed by GitHub
parent 8b09248bf3 4c01a8d6f5
commit f4591ba867
3 changed files with 5 additions and 5 deletions
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qdrant-landing/content/articles/vector-search-filtering.md
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@@ -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)
[![qdrant-hybrid-cloud](/docs/homepage/cloud-cta.png)](https://qdrant.to/cloud)
qdrant-landing/content/documentation/concepts/points.md
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@@ -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. <br> Useful for exact token match and collaborative filtering recommendations. |
| MultiVectors | Matrices of numbers with fixed length but variable height. <br> Usually obtained from late interraction models like ColBERT. |
| MultiVectors | Matrices of numbers with fixed length but variable height. <br> 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.
qdrant-landing/content/documentation/guides/installation.md
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@@ -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