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Add note about inference being Cloud-only

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Abdon Pijpelink
2025-12-22 10:35:32 +01:00
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qdrant-landing/content/documentation/guides/text-search.md
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@@ -13,6 +13,10 @@ Qdrant is a vector search engine, making it a great tool for [semantic search](#
Semantic search is a search technique that focuses on the meaning of the text rather than just matching on keywords. This is achieved by converting text into [vectors](/documentation/concepts/vectors/) (embeddings) using machine learning models. These vectors capture the semantic meaning of the text, enabling you to find similar text even if it doesn't share exact keywords.
<aside role="status">
The examples in this guide use <a href="/documentation/concepts/inference">inference</a> to let Qdrant generate the vectors. Inference is only available on <a href="/documentation/concepts/inference/#qdrant-cloud-inference">Qdrant Cloud</a>, with the exception of the BM25 model. If you are not running on Qdrant Cloud, you can use a library like <a href="/documentation/fastembed/">FastEmbed</a> to generate vectors on the client side.
</aside>
For example, to search through a collection of books, you could use a model like the `all-MiniLM-L6-v2` sentence transformer model. First, create a collection and configure a dense vector for the book descriptions:
{{< code-snippet path="/documentation/headless/snippets/text-search/create-description-dense-collection/" >}}
@@ -25,7 +29,7 @@ To find books related to "time travel", use the following query:
{{< code-snippet path="/documentation/headless/snippets/text-search/query-description-dense/" >}}
Note that these examples do not provide explicit vectors. Instead, the requests use [inference](/documentation/concepts/inference) to let Qdrant generate vectors from the `text` provided in the request using the specified `model`. Alternatively, you can generate explicit vectors on the client side using a library like [FastEmbed](/documentation/fastembed/).
In these examples, Qdrant uses [inference](/documentation/concepts/inference) to generate vectors from the `text` provided in the request using the specified `model`. Alternatively, you can generate vectors on the client side with a library like [FastEmbed](/documentation/fastembed/).
### Lexical Search