setup.md

Supabase

Supabase offers an easy and efficient way to store vectors via pgvector extension for Postgres Database. You can use Supabase CLI to set up a whole Supabase stack locally or in the cloud or you can also use docker-compose, k8s and other options available. For a hosted/managed solution, try Supabase.com and unlock the full power of Postgres with built-in authentication, storage, auto APIs, and Realtime features. See more helpful examples of Supabase & pgvector as a vector database here.

• The database needs the pgvector extension, which is included in Supabase distribution of Postgres.
• It is possible to provide a Postgres connection string and an app will add documents table, query Postgres function, and pgvector extension automatically.
• But it is recommended to separate the migration process from an app. And execute the migration script in a different pipeline by using SQL statements from _init_db() function in Supabase datastore provider.

Retrieval App Environment Variables

Name	Required	Description
DATASTORE	Yes	Datastore name. Set this to supabase
BEARER_TOKEN	Yes	Your secret token
OPENAI_API_KEY	Yes	Your OpenAI API key

Supabase Datastore Environment Variables

Name	Required	Description	Default
SUPABASE_URL	Yes	Supabase Project URL
SUPABASE_ANON_KEY	Optional	Supabase Project API anon key
SUPABASE_SERVICE_ROLE_KEY	Optional	Supabase Project API service key, will be used if provided instead of anon key

Supabase Datastore local development & testing

In order to test your changes to the Supabase Datastore, you can run the following commands:

1. Install Supabase CLI and Docker

2. Run the Supabase start command from examples/providers directory. Config for Supabase local setup is available in examples/providers/supabase directory with required migrations.

# Run the Supabase stack using cli in docker
# go to examples/providers and run supabase start
cd examples/providers
supabase start

3. Supabase start will download docker images and launch Supabase stack locally. You will see similar output:

Applying migration 20230414142107_init_pg_vector.sql...
Seeding data supabase/seed.sql...
Started supabase local development setup.

         API URL: http://localhost:54321
          DB URL: postgresql://postgres:postgres@localhost:54322/postgres
      Studio URL: http://localhost:54323
    Inbucket URL: http://localhost:54324
      JWT secret: super-secret-jwt-token-with-at-least-32-characters-long
        anon key: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZS1kZW1vIiwicm9sZSI6ImFub24iLCJleHAiOjE5ODM4MTI5OTZ9.CRXP1A7WOeoJeXxjNni43kdQwgnWNReilDMblYTn_I0
service_role key: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZS1kZW1vIiwicm9sZSI6InNlcnZpY2Vfcm9sZSIsImV4cCI6MTk4MzgxMjk5Nn0.EGIM96RAZx35lJzdJsyH-qQwv8Hdp7fsn3W0YpN81IU

4. Export environment variables required for the Supabase Datastore

export SUPABASE_URL=http://localhost:54321
export SUPABASE_SERVICE_ROLE_KEY='eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZS1kZW1vIiwicm9sZSI6InNlcnZpY2Vfcm9sZSIsImV4cCI6MTk4MzgxMjk5Nn0.EGIM96RAZx35lJzdJsyH-qQwv8Hdp7fsn3W0YpN81IU'

5. Run the Supabase datastore tests from the project's root directory

# Run the Supabase datastore tests
# go to project's root directory and run
poetry run pytest -s ./tests/datastore/providers/supabase/test_supabase_datastore.py

6. When you go to prod (if cloud hosted) it is recommended to link your supabase project with the local setup from examples/providers/supabase. All migrations will be synced with the cloud project after you run supabase db push. Or you can manually apply migrations from examples/providers/supabase/migrations directory.

7. You might want to add RLS policies to the documents table. Or you can just continue using it on the server side only with the service role key. But you should not use service role key on the client side in any case.

Indexes for Postgres

By default, pgvector performs exact nearest neighbor search. To speed up the vector comparison, you may want to create indexes for the embedding column in the documents table. You should do this only after a few thousand records are inserted.

As datasotre is using inner product for similarity search, you can add index as follows:

create index on documents using ivfflat (embedding vector_ip_ops) with (lists = 100);

To choose lists constant - a good place to start is records / 1000 for up to 1M records and sqrt(records) for over 1M records

For more information about indexes, see pgvector docs.