---
title: OpenCLIP
sidebarTitle: OpenCLIP
---

import {
  PyEmbeddingOpenclipSetup,
  PyEmbeddingOpenclipTextSearch,
  PyEmbeddingOpenclipImageSearch,
} from '/snippets/integrations.mdx';

We support CLIP model embeddings using the open source alternative, [open-clip](https://github.com/mlfoundations/open_clip) which supports various customizations. It is registered as `open-clip` and supports the following customizations:

| Parameter | Type | Default Value | Description |
|---|---|---|---|
| `name` | `str` | `"ViT-B-32"` | The name of the model. |
| `pretrained` | `str` | `"laion2b_s34b_b79k"` | The name of the pretrained model to load. |
| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. |
| `batch_size` | `int` | `64` | The number of images to process in a batch. |
| `normalize` | `bool` | `True` | Whether to normalize the input images before feeding them to the model. |

This embedding function supports ingesting images as both bytes and urls. You can query them using both text and other images.

<Info>
LanceDB supports ingesting images directly from accessible links.
</Info>

<CodeGroup>
  <CodeBlock filename="Python" language="Python" icon="python">
    {PyEmbeddingOpenclipSetup}
  </CodeBlock>
</CodeGroup>
Now we can search using text from both the default vector column and the custom vector column
<CodeGroup>
  <CodeBlock filename="Python" language="Python" icon="python">
    {PyEmbeddingOpenclipTextSearch}
  </CodeBlock>
</CodeGroup>

Because we're using a multimodal embedding function, we can also search using images

<CodeGroup>
  <CodeBlock filename="Python" language="Python" icon="python">
    {PyEmbeddingOpenclipImageSearch}
  </CodeBlock>
</CodeGroup>