`SupabaseVectorBucketDocumentStore` and `SupabaseVectorBucketEmbeddingRetriever`

[davidsbatista]

Implement SupabaseVectorBucketDocumentStore and SupabaseVectorBucketEmbeddingRetriever backed by Supabase's S3-compatible Vector Buckets.

⚠️ Supabase Vector Buckets are currently in alpha upstream. This component should be clearly marked as experimental.

Detailed design

Suitable for large-scale, latency-tolerant workloads where cost is the primary concern (similar to turbopuffer or TopK). Uses the Supabase Storage API or S3-compatible endpoint.

from haystack_integrations.document_stores.supabase import SupabaseVectorBucketDocumentStore

document_store = SupabaseVectorBucketDocumentStore(
    supabase_url="https://<project>.supabase.co",
    supabase_key=Secret.from_env_var("SUPABASE_SERVICE_KEY"),
    bucket_name="my-vector-bucket",
    embedding_dimension=1536,
)

Implementation notes

• Primary dependency: supabase-py
• Auth via supabase_url and supabase_key (using Secret for secure handling)
• Should be gated behind an explicit opt-in or marked experimental in package metadata and docstrings
• Monitor Supabase Vector Buckets upstream status before stabilising the API
• Integration lives in integrations/supabase/

Checklist

• The code is documented with docstrings and was merged into a feature branch

[srini047]

@davidsbatista I would like to work on this issue, I have the context. But one doubt is should I wait for this PR to be reviewed and merged #3164 since the baseline changes for a new integration is already here. WDYT?

Also would be great if you can clarify this comment: https://github.com/deepset-ai/haystack-core-integrations/pull/3164/changes#r3091479058