Merge pull request #14724 from supabase/chore/vector_tagline

new vector tagline?

apps/docs/pages/guides/ai.mdx

@@ -3,21 +3,16 @@ import Layout from '~/layouts/DefaultGuideLayout'
 export const meta = {
   id: 'ai',
   title: 'AI & Vectors',
-  description: 'Use Supabase to store and search embedding vectors.',
-  subtitle: 'Use Supabase to store and search embedding vectors.',
+  description: 'The best vector database is the database you already have.',
+  subtitle: 'The best vector database is the database you already have.',
   sidebar_label: 'Overview',
 }
 
-Supabase provides a number of tools to get started with Vectors and Embeddings. These tools include:
+Supabase provides an open source toolkit for developing AI applications using Postgres and pgvector. Use the Supabase client libraries to store, index, and query your vector embeddings at scale.
 
-- An embedding store using the popular [pgvector](https://github.com/pgvector/pgvector/)
-- A Python Library for managing unstructured collections called [Vecs](https://supabase.github.io/vecs/api)
+The toolkit includes:
 
-## Features
-
-Supabase provides a full toolkit for developing AI applications.
-
-- A [vector store](/docs/guides/ai/vector-columns) and embeddings support using pgvector.
+- A [vector store](/docs/guides/ai/vector-columns) and embeddings support using Postgres and pgvector.
 - A [Python client](/docs/guides/ai/vecs-python-client) for managing unstructured embeddings.
 - [Database migrations](/docs/guides/ai/examples/headless-vector-search#prepare-your-database) for managing structured embeddings.
 - Integrations with all popular AI providers, such as [OpenAI](/docs/guides/ai/examples/openai), [Hugging Face](/docs/guides/ai/hugging-face), [LangChain](/docs/guides/ai/langchain), and more.
@@ -74,6 +69,41 @@ export const examples = [
   },
 ]
 
+## Integrations
+
+<div className="grid md:grid-cols-12 gap-4 not-prose">
+  {integrations.map((x) => (
+    <div className="col-span-4" key={x.href}>
+      <Link href={x.href} passHref>
+        <a>
+          <GlassPanel title={x.name}>{x.description}</GlassPanel>
+        </a>
+      </Link>
+    </div>
+  ))}
+</div>
+
+export const integrations = [
+  {
+    name: 'OpenAI',
+    description:
+      'OpenAI is an AI research and deployment company. Supabase provides a simple way to use OpenAI in your applications.',
+    href: '/docs/guides/ai/examples/building-chatgpt-plugins',
+  },
+  {
+    name: 'Hugging Face',
+    description:
+      "Hugging Face is an open-source provider of NLP technologies. Supabase provides a simple way to use Hugging Face's models in your applications.",
+    href: '/docs/guides/ai/hugging-face',
+  },
+  {
+    name: 'LangChain',
+    description:
+      'LangChain is a language-agnostic, open-source, and self-hosted API for text translation, summarization, and sentiment analysis.',
+    href: '/docs/guides/ai/langchain',
+  },
+]
+
 ## Case studies
 
 <div className="grid md:grid-cols-12 gap-4 not-prose">

apps/www/data/products/vector/pageData.tsx

@@ -18,8 +18,11 @@ export default {
     ),
     subheader: (
       <>
-        Use pgvector with Supabase client libraries <br className="hidden md:block" />
-        to store, index, and query your vector embeddings at scale.
+        Use the Supabase client libraries to store, index, and query your vector embeddings at
+        scale.
+        <br className="hidden md:block" />
+        Build AI applications with your Postgres and pgvector. The best vector database is the
+        database you already have.
       </>
     ),
     image: '/images/product/vector/vector-hero.svg',

apps/www/pages/vector/Vector.tsx

@@ -21,9 +21,9 @@ const EnterpriseCta = dynamic(() => import('~/components/Sections/EnterpriseCta'
 function VectorPage() {
   // base path for images
   const { basePath } = useRouter()
-  const meta_title = 'Vector | The open source vector toolkit for Postgres'
+  const meta_title = 'Supabase Vector | The open source vector toolkit for Postgres.'
   const meta_description =
-    'Integrate Supabase Vector database with your favorite ML-models to store, index and access vector embeddings for any AI use case.'
+    'An open source toolkit for developing AI applications using Postgres and pgvector. Integrate with your favorite ML-models to store, index, and access vector embeddings for any AI use case.'
 
   return (
     <>