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docs/en/getting-started/example-datasets/laion.md
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The [Laion-400M dataset](https://laion.ai/blog/laion-400-open-dataset/) contains 400 million images with English image captions. Laion nowadays provides [an even larger dataset](https://laion.ai/blog/laion-5b/) but working with it will be similar.
The dataset contains the image URL, embeddings for both the image and the image caption, a similarity score betwen the image and the image caption, as well as metadata, e.g. the image width/height, the licence and a NSFW flag. We can use the dataset to demonstrate [approximate nearest neighbor search](../../engines/table-engines/mergetree-family/annindexes.md) in ClickHouse.
The dataset contains the image URL, embeddings for both the image and the image caption, a similarity score between the image and the image caption, as well as metadata, e.g. the image width/height, the licence and a NSFW flag. We can use the dataset to demonstrate [approximate nearest neighbor search](../../engines/table-engines/mergetree-family/annindexes.md) in ClickHouse.
## Data preparation
@ -59,7 +59,7 @@ To start the data preparation pipeline, run:
seq 0 409 | xargs -P100 -I{} bash -c './download.sh {}'
```
The dataset is splitted into 410 files, each file contains ca. 1 million rows. If you like to work with a smaller subset of the data, simply adjust the limits, e.g. `seq 0 9 | ...`.
The dataset is split into 410 files, each file contains ca. 1 million rows. If you like to work with a smaller subset of the data, simply adjust the limits, e.g. `seq 0 9 | ...`.
## Create table
@ -163,7 +163,7 @@ The speed increased significantly at the cost of less accurate results. This is
## Creating embeddings with UDFs
One usually wants to create embeddings for new images or new image captions and search for similar image / imgage caption pairs in the data. We can use [UDF](../../sql-reference/functions/index.md#sql-user-defined-functions) to create the `target` vector without leaving the client. It is important to use the same model to create the data and new embeddings for searches. The following scripts utilize the `ViT-B/32` model which also underlies the dataset.
One usually wants to create embeddings for new images or new image captions and search for similar image / image caption pairs in the data. We can use [UDF](../../sql-reference/functions/index.md#sql-user-defined-functions) to create the `target` vector without leaving the client. It is important to use the same model to create the data and new embeddings for searches. The following scripts utilize the `ViT-B/32` model which also underlies the dataset.
### Text embeddings