diff --git a/apps/docs/components/Navigation/NavigationMenu/NavigationMenu.constants.ts b/apps/docs/components/Navigation/NavigationMenu/NavigationMenu.constants.ts
index 8ff54ba58c6..71029382f99 100644
--- a/apps/docs/components/Navigation/NavigationMenu/NavigationMenu.constants.ts
+++ b/apps/docs/components/Navigation/NavigationMenu/NavigationMenu.constants.ts
@@ -357,6 +357,11 @@ export const SocialLoginItems = [
icon: '/docs/img/icons/discord-icon',
url: '/guides/auth/social-login/auth-discord',
},
+ {
+ name: 'Figma',
+ icon: '/docs/img/icons/figma-icon',
+ url: '/guides/auth/social-login/auth-figma',
+ },
{
name: 'Kakao',
icon: '/docs/img/icons/kakao-icon',
diff --git a/apps/docs/data/authProviders.ts b/apps/docs/data/authProviders.ts
index 9694f1daf12..6a9a4b4d30c 100644
--- a/apps/docs/data/authProviders.ts
+++ b/apps/docs/data/authProviders.ts
@@ -49,6 +49,16 @@ const authProviders = [
selfHosted: true,
authType: 'social',
},
+ {
+ name: 'Figma',
+ logo: '/docs/img/icons/figma-icon',
+ href: '/guides/auth/social-login/auth-figma',
+ official: true,
+ supporter: 'Supabase',
+ platform: true,
+ selfHosted: true,
+ authType: 'social',
+ },
{
name: 'GitHub',
logo: '/docs/img/icons/github-icon',
diff --git a/apps/docs/pages/guides/ai/choosing-compute-addon.mdx b/apps/docs/pages/guides/ai/choosing-compute-addon.mdx
index 1a27b1e52cd..4ab4e13e808 100644
--- a/apps/docs/pages/guides/ai/choosing-compute-addon.mdx
+++ b/apps/docs/pages/guides/ai/choosing-compute-addon.mdx
@@ -4,29 +4,24 @@ export const TabPanel = Tabs.Panel
export const meta = {
id: 'ai-choosing-compute-addon',
- title: 'Choosing Compute Add-on',
- description: 'Choosing the right Compute Add-on for your workload.',
- subtitle: 'Choosing the right Compute Add-on for your workload.',
+ title: 'Choosing your Compute Add-on',
+ description: 'Choosing the right Compute Add-on for your vector workload.',
+ subtitle: 'Choosing the right Compute Add-on for your vector workload.',
sidebar_label: 'Choosing Compute Add-on',
}
-This guide will help you choose the right Compute Add-on for your workload. We'll provide general guidance, as it is impossible to provide specific instructions for every possible use case. The goal is to give you a starting point from which you can make your own benchmarks and optimizations.
+You have two options for scaling your vector workload:
-Note that it is only useful for index searches, not for sequential scans. Sequential scans will result to significantly higher latencies and lower throughput, but will guarantee 100% precision and will not be RAM bound. Therefore it is possible to use a smaller plan for sequential scans.
+1. Increase the size of your database. This guide will help you choose the right size for your workload.
+2. Spread your workload across multiple databases. You can find more details about this approach in [Engineering for Scale](engineering-for-scale).
-For more information about engineering at scale, see our [Engineering for Scale](/docs/guides/ai/engineering-for-scale) guide.
+## Dimensionality
-## Simple workloads
+The number of dimensions in your embeddings is the most important factor in choosing the right Compute Add-on. In general, the lower the dimensionality the better the performance. We've provided guidance for some of the more common embedding dimensions below. For each benchmark, we used [Vecs](https://github.com/supabase/vecs) to create a collection, upload the embeddings to a single table, and create an `inner-product` index for the embedding column. We then ran a series of queries to measure the performance of different compute add-ons:
-We've run a set of benchmarks using
+### 1536 Dimensions
-- The [dbpedia-entities-openai-1M](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M) dataset. This dataset contains 1,000,000 embeddings for text, with each embedding being 1536 dimensions made using OpenAI API.
-- The [gist-960-angular](http://corpus-texmex.irisa.fr/) dataset. This dataset contains 1,000,000 embeddings for images, with each embedding being 960 dimensions.
-- The [GloVe Reddit comments](https://nlp.stanford.edu/projects/glove/) dataset, which contains 1,623,397 embeddings for words, with each embedding being 512 dimensions.
-
-We used [Vecs](https://github.com/supabase/vecs) to create a collection, upload the embeddings to a single table, and create an `inner-product` index for the embedding column. We then ran a series of queries to measure the performance of different compute add-ons:
-
-### Results
+This benchmark uses the [dbpedia-entities-openai-1M](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M) dataset, which contains 1,000,000 embeddings of text. Each embedding is 1536 dimensions created with the [OpenAI Embeddings API](https://platform.openai.com/docs/guides/embeddings).
+
+
+
+