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Basic structure for the overview page
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@@ -4,20 +4,51 @@ export const meta = {
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id: 'ai',
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title: 'AI & Vectors',
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description: 'Use Supabase to store and search embedding vectors.',
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subtitle: 'Use Supabase to store and search embedding vectors.',
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sidebar_label: 'Overview',
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video: 'https://www.youtube.com/v/J9mTPY8rIXE',
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}
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Supabase AI & Vectors makes it simple to store and search embeding vectors.
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Supabase provides a number of tools to get started with Vectors and Embeddings. These tools include:
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## TBD
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- An embedding store using the popular [pgvector](https://github.com/pgvector/pgvector/)
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- A Python Library for managing collections called [Vecs](https://supabase.github.io/vecs)
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TBD
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## Examples
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## See also
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Check out all of the AI [templates and examples](https://github.com/supabase/supabase/tree/master/examples/ai) in our GitHub repository.
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- TBD
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<div className="grid md:grid-cols-12 gap-4 not-prose">
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{examples.map((x) => (
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<div className="col-span-4" key={x.href}>
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<Link href={x.href} passHref>
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<a>
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<GlassPanel icon={'/docs/img/icons/github-icon'} hasLightIcon={true} title={x.name}>
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{x.description}
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</GlassPanel>
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</a>
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</Link>
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</div>
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))}
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</div>
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export const Page = ({ children }) => <Layout meta={meta} children={children} />
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export const examples = [
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{
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name: 'With supabase-js',
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description: 'Use the Supabase client inside your Edge Function.',
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href: '/guides/functions/auth',
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},
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{
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name: 'Type-Safe SQL with Kysely',
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description: 'Combining Kysely with Deno Postgres',
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href: '/guides/functions/kysely-postgres',
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},
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{
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name: 'With CORS headers',
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description: 'Send CORS headers for invoking from the browser.',
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href: '/guides/functions/cors',
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},
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]
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export const Page = ({ children }) => <Layout meta={meta} children={children} hideToc={true} />
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export default Page
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