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As described in Structured & Unstructured Embeddings, AI workloads come in many forms.
For data science or ephemeral workloads, the Supabase Vecs client gets you started. You need a connection string and vecs handles setting up your database to store and query vectors with associated metadata.
Note
Click Connect at the top of any project page to get your connection string.
Copy the URI from the Shared pooler option.
For production python applications with version controlled migrations, we recommend adding first class vector support to your toolchain by registering the vector type with your ORM. pgvector provides bindings for the most commonly used SQL drivers/libraries including Django, SQLAlchemy, SQLModel, psycopg, asyncpg and Peewee.