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21 lines
1.1 KiB
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21 lines
1.1 KiB
Plaintext
---
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id: 'ai-python-clients'
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title: 'Choosing a Client'
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description: 'Learn how to manage vectors using Python'
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sidebar_label: 'Choosing a Client'
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---
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As described in [Structured & Unstructured Embeddings](/docs/guides/ai/structured-unstructured), AI workloads come in many forms.
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For data science or ephemeral workloads, the [Supabase Vecs](https://supabase.github.io/vecs/) client gets you started. All you need is a connection string and vecs handles setting up your database to store and query vectors with associated metadata.
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<Admonition type="tip">
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Click [**Connect**](/dashboard/project/_/?showConnect=true) at the top of any project page to get your connection string.
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Copy the URI from the **Shared pooler** option.
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</Admonition>
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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](https://github.com/pgvector/pgvector-python). pgvector provides bindings for the most commonly used SQL drivers/libraries including Django, SQLAlchemy, SQLModel, psycopg, asyncpg and Peewee.
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