Merge pull request #15546 from supabase/guide/ai-404

fix: broken link in AI vector guide

apps/docs/pages/guides/ai/managing-collections.mdx

@@ -9,7 +9,7 @@ export const meta = {
 
 A collection is a group of vector records managed by the `vecs` Python library. Records can be added to or updated in a collection. Collections can be queried at any time, but should be indexed for scalable query performance.
 
-Supabase provides a [Python client](/docs/guides/ai/vecs-python-client) called `vecs` for managing unstructured vector stores in Postgres. If you come from a data science background, this unstructured data approach will feel familiar. If you are more interested in a structured data approach, see [Vector columns](/docs/guides/ai/vector-columns) or read our guide on [Structured & Unstructured Embeddings](/docs/guides/ai/structured-unstructured-embeddings).
+Supabase provides a [Python client](/docs/guides/ai/vecs-python-client) called `vecs` for managing unstructured vector stores in Postgres. If you come from a data science background, this unstructured data approach will feel familiar. If you are more interested in a structured data approach, see [Vector columns](/docs/guides/ai/vector-columns) or read our guide on [Structured & Unstructured Embeddings](/docs/guides/ai/structured-unstructured).
 
 Under the hood `vecs` will manage the necessary Postgres tables and columns to store and query your collections.
 

apps/www/lib/redirects.js

@@ -2061,4 +2061,9 @@ module.exports = [
     source: '/docs/guides/database/extensions/pgrepack',
     destination: '/docs/guides/database/extensions/pg_repack',
   },
+  {
+    permanent: true,
+    source: '/docs/guides/ai/structured-unstructured-embeddings',
+    destination: '/docs/guides/ai/structured-unstructured',
+  },
 ]