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Jack Ye
b21fb2a4dc
The pages describing this code lived in lancedb/docs, a repository with ten stars that neither contributing guide mentioned. A user who hit a wrong page while reading the source had to find a separate, near-invisible repository to fix it. These 102 pages now sit beside the code, and a documentation fix is a pull request in the repository the reader already had open. `docs/web` is a complete Mintlify site: `cd docs/web && mint dev` serves it with no other checkout. Enterprise pages stay private and are merged in when the published site is assembled, so links into them do not resolve here — every open-source page does. `docs/web-tests` holds the tests the code examples are extracted from. They ran against a published wheel before; here they can run against this repository's own SDK, so an example that stops compiling fails the pull request that broke it rather than being noticed later. Both directories sit beside `docs/src` and are invisible to it: mkdocs reads `docs_dir: src`, so the SDK reference builds and deploys exactly as before.
55 lines
1.8 KiB
Plaintext
55 lines
1.8 KiB
Plaintext
---
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title: ImageBind
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sidebarTitle: ImageBind
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---
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import {
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PyEmbeddingImagebindSetup,
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PyEmbeddingImagebindImageSearch,
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PyEmbeddingImagebindAudioSearch,
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PyEmbeddingImagebindTextSearch,
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} from '/snippets/integrations.mdx';
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We have support for [imagebind](https://github.com/facebookresearch/ImageBind) model embeddings. You can download our version of the packaged model via - `pip install imagebind-packaged==0.1.2`.
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This function is registered as `imagebind` and supports Audio, Video and Text modalities(extending to Thermal,Depth,IMU data):
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| Parameter | Type | Default Value | Description |
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|---|---|---|---|
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| `name` | `str` | `"imagebind_huge"` | Name of the model. |
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| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. |
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| `normalize` | `bool` | `False` | set to `True` to normalize your inputs before model ingestion. |
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Below is an example demonstrating how the API works:
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyEmbeddingImagebindSetup}
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</CodeBlock>
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</CodeGroup>
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Now, we can search using any modality:
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#### image search {#image-search}
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyEmbeddingImagebindImageSearch}
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</CodeBlock>
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</CodeGroup>
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#### audio search {#audio-search}
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyEmbeddingImagebindAudioSearch}
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</CodeBlock>
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</CodeGroup>
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#### Text search {#text-search}
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You can add any input query and fetch the result as follows:
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyEmbeddingImagebindTextSearch}
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</CodeBlock>
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</CodeGroup>
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If you have any questions about the embeddings API, supported models, or see a relevant model missing, please raise an issue [on GitHub](https://github.com/lancedb/lancedb/issues).
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