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47 lines
1.8 KiB
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47 lines
1.8 KiB
Plaintext
---
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title: 'Vector Bucket Local Development'
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subtitle: 'Develop and test vector bucket integrations in your local environment with the Supabase CLI.'
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---
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<Admonition type="caution" title="This feature is in alpha">
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Expect rapid changes, limited features, and possible breaking updates. [Share feedback](https://github.com/orgs/supabase/discussions/40116) as we refine the experience and expand access.
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</Admonition>
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You can now develop and test Vector Bucket integrations in your local environment using the Supabase CLI.
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This allows you to build and iterate on your vector search applications without needing to deploy to a live environment.
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Make sure you have the latest version of the Supabase CLI installed to access this feature.
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<Admonition type="note" title="Local driver">
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Local vector buckets use pgvector as their storage engine. Hosted vector buckets use Amazon S3 Vectors, so query behavior and performance can differ between environments.
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Hosted `queryVectors` requests accept `topK` values up to 10,000 and return results in pages of at most 100. Use `nextToken` to retrieve each additional page. Local requests accept `topK` values up to 100 and reject `nextToken`.
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See [Vector Bucket limits](/docs/guides/storage/vector/limits) for the full comparison.
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</Admonition>
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## Setting up local vector buckets
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Make sure you have the feature enabled in your `config.toml` file:
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```toml
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# Store vector embeddings in S3 for large and durable datasets
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[storage.vector]
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enabled = true
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```
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### Declarative configuration
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You can define your vector buckets in the `config.toml` file using the following syntax:
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```toml
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[storage.vector.buckets.documents-openai]
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[storage.vector.buckets.images]
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```
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Then use `supabase seed buckets` to create the buckets in your local environment or linked project.
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