krzysztof/landing_page
1
0
Fork 0
mirror of https://github.com/qdrant/landing_page.git synced 2026-10-04 10:28:29 +02:00
Code Issues Packages Projects Releases Wiki Activity

Small typo, grammar and style fixes

Browse Source
This commit is contained in:
Bastian Hofmann
2025-07-15 11:55:48 +02:00
parent 778f365d36
commit efa37e545b
4 changed files with 31 additions and 35 deletions
Download Patch File Download Diff File Expand all files Collapse all files
qdrant-landing/content/documentation/headless/snippets/cloud-inference/image/_description.md
+4 -4
View File
@@ -1,7 +1,7 @@
This code snippet shows how to use Qdrant Cloud's cloud-side inference to automatically generate vector embeddings from images and text during upsert and search operations.
This code snippet shows how to use Qdrant Cloud Inference to automatically generate vector embeddings from images and text during upsert and search operations.
In the example, a new point is inserted with an image URL and a specified model. The vector embedding is generated on the Qdrant Cloud side using the provided model. The `image` and `model` fields specify the image to embed and the model to use.
In this example, a new point is inserted with an image URL and a specified model. The vector embedding is generated on the Qdrant Cloud side using the provided model. The `image` and `model` fields specify the image to embed and the model to use.
After the point is inserted, it becomes searchable. The snippet also demonstrates how to perform a search query using cloud-side inference: a `text` and `model` are provided, and Qdrant Cloud generates the query vector automatically. This allows you to search your collection using natural language queries without manually generating embeddings.
After the point is inserted, it becomes searchable. The snippet also demonstrates how to perform a search query using Qdrant Cloud Inference. A `text` and `model` are provided, and Qdrant Cloud generates the query vector automatically. This allows you to search your collection using natural language queries without manually generating embeddings.
Example demonstrates multimodal search with `CLIP` model and cloud-side inference.
This example demonstrates multimodal search using the `CLIP` model and Qdrant Cloud Inference.
qdrant-landing/content/documentation/headless/snippets/cloud-inference/image/bash.md
+1 -1
View File
@@ -8,7 +8,7 @@ curl -X PUT "https://xyz-example.qdrant.io:6333/collections/<your-collection>/po
{
"id": 1,
"vector": {
"text": "https://qdrant.tech/example.png",
"image": "https://qdrant.tech/example.png",
"model": "qdrant/clip-vit-b-32-vision"
},
"payload": {
qdrant-landing/content/documentation/headless/snippets/cloud-inference/simple/_description.md
+4 -7
View File
@@ -1,8 +1,5 @@
This code snippet demonstrates how to use cloud inference in Qdrant Cloud to automatically create vector embeddings from text documents during upseart and query operations.
In this example we create a new point with a new vector, generated on the qdrant cloud side.
`Document` object contains the text which will be used as an input for inference model.
Specific model which should be used for inference is defined in the `model` parameter.
This code snippet demonstrates how to use cloud inference in Qdrant Cloud to automatically create vector embeddings from text documents during upsert and query operations. In this example we create a new point with a new vector, generated in Qdrant Cloud.
After point is inserted is becomes searchable.
Snippet contains an example of search query request, that uses cloud-side inferene.
`Document` object is used to obtain query vector.
The `Document` object contains the text which will be used as an input for inference model. The model which should be used for inference is defined in the `model` parameter.
After the point is inserted it becomes searchable. The snippet contains an example of search query request, that uses Qdrant Cloud inferene. The `Document` object is used to create the query vector with the configured inference `model`.