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## I have read the [CONTRIBUTING.md](https://github.com/supabase/supabase/blob/master/CONTRIBUTING.md) file. YES <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **Documentation** * Updated examples and guides to use Supabase publishable (client) keys instead of anon keys for client-side usage across frameworks and platforms. * Renamed environment variable examples and .env templates to reflect publishable key naming. * Adjusted sample requests and client-init examples to send/use the publishable key via the apikey header where applicable. * Updated references from service_role to secret for server-side credential guidance. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: fadymak <fady@fadymak.com>
31 lines
1.9 KiB
Markdown
31 lines
1.9 KiB
Markdown
# AI Inference in Supabase Edge Functions
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Since Supabase Edge Runtime [v1.36.0](https://github.com/supabase/edge-runtime/releases/tag/v1.36.0) you can run the [`gte-small` model](https://huggingface.co/Supabase/gte-small) natively within Supabase Edge Functions without any external dependencies! This allows you to easily generate text embeddings without calling any external APIs!
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## Semantic Search with pgvector and Supabase Edge Functions
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This demo consists of three parts:
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1. A [`generate-embedding`](./supabase/functions/generate-embedding/index.ts) database webhook edge function which generates embeddings when a content row is added (or updated) in the [`public.embeddings`](./supabase/migrations/20240408072601_embeddings.sql) table.
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2. A [`query_embeddings` Postgres function](./supabase/migrations/20240410031515_vector-search.sql) which allows us to perform similarity search from an egde function via [Remote Procedure Call (RPC)](https://supabase.com/docs/guides/database/functions?language=js).
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3. A [`search` edge function](./supabase/functions/search/index.ts) which generates the embedding for the search term, performs the similarity search via RPC function call, and returns the result.
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## Deploy
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- Link your project: `supabase link`
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- Deploy Edge Functions: `supabase functions deploy`
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- Update project config to [enable webhooks](https://supabase.com/docs/guides/local-development/cli/config#experimental.webhooks.enabled): `supabase config push`
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- Navigate to the [database-webhook](./supabase/migrations/20240410041607_database-webhook.sql) migration file and insert your `generate-embedding` function details.
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- Push up the database schema `supabase db push`
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## Run
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Run a search via curl POST request:
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```bash
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curl -i --location --request POST 'https://<PROJECT-REF>.supabase.co/functions/v1/search' \
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--header 'apikey: <SUPABASE_PUBLISHABLE_KEY>' \
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--header 'Content-Type: application/json' \
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--data '{"search":"vehicles"}'
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```
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