diff --git a/apps/docs/pages/guides/ai/managing-collections.mdx b/apps/docs/pages/guides/ai/managing-collections.mdx index 58e700e2927..5ed4d45ce42 100644 --- a/apps/docs/pages/guides/ai/managing-collections.mdx +++ b/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. diff --git a/apps/www/lib/redirects.js b/apps/www/lib/redirects.js index ff6634e85c6..a1c27c40b70 100644 --- a/apps/www/lib/redirects.js +++ b/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', + }, ]