# Platform Vector Store

A dedicated vector store the platform manages for you. Point an embed pipeline at it and search your content semantically. Zero setup.

## What this connection offers

Use managed semantic-search storage without configuring an external vector database. Keep the embedding model and index compatible, and distinguish removal of vectors from deletion of original files.

| Detail | Value |
| --- | --- |
| Available uses | similarity index |
| Sign-in | Managed by Strive |
| Catalogue identifier | `platform_vectorstore` |
| Connection scope | tenant |
| Direction | destination |

## Connect

1. Open **Connections**, choose to connect a service, and select **Platform Vector Store** with the uses listed above.
2. Give the connection a recognisable name.
3. Complete creation; no external secret is required for this entry.
4. Check the connection status and run a connection test where offered.
5. Select this saved connection in the app, workflow step, storage browser, or Assistant task that needs it.

## Connection settings

This connector declares no ordinary configuration fields. Its sign-in or managed provisioning flow supplies the access it needs.

## Diagnose a failure

Check the connected account, its permission to the target, the selected folder, table or collection, and the connection’s current status. A successful sign-in is not proof that a particular resource is shared with that account. An empty search or folder page is not necessarily an authentication error. Revoke access when it is no longer needed; deleting the connection can also affect its tools and synced data. See [connection lifecycle](/docs/connections/revoke-and-delete).