# Vector Bucket Local Development

Develop and test vector bucket integrations in your local environment with the Supabase CLI.

Caution: 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.

You can now develop and test Vector Bucket integrations in your local environment using the Supabase CLI.

This allows you to build and iterate on your vector search applications without needing to deploy to a live environment.
Make sure you have the latest version of the Supabase CLI installed to access this feature.

Note: 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.

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`.

See [Vector Bucket limits](/docs/guides/storage/vector/limits) for the full comparison.

## Setting up local vector buckets

Make sure you have the feature enabled in your `config.toml` file:

```toml

# Store vector embeddings in S3 for large and durable datasets
[storage.vector]
enabled = true
```

### Declarative configuration

You can define your vector buckets in the `config.toml` file using the following syntax:

```toml
[storage.vector.buckets.documents-openai]
[storage.vector.buckets.images]
```

Then use `supabase seed buckets` to create the buckets in your local environment or linked project.