Collection Lifecycle

Not every collection needs to serve low-latency vector search all the time. Seasonal campaigns, completed projects, and long-tail archives can move to cheaper tiers without losing the data or re-processing anything to bring them back.

Lifecycle states

State	Vector search	Storage	Restorable
`active` (default)	Fast, fully indexed	Hot vector index	n/a
`cold`	Evicted from the hot index; vectors preserved in object storage	Object-storage backup	Yes, rehydrate to `active` at any time
`archived`	Removed	Vectors permanently deleted; documents’ metadata remains	No

Allowed transitions: active → cold, active → archived, cold → active, cold → archived. Archival is permanent: the vectors are deleted.

Going cold keeps documents and metadata. It evicts the collection’s vectors from the hot index after backing them up. Rehydrating restores search without re-running any extraction or embedding.

Check lifecycle status

curl "https://api.mixpeek.com/v1/collections/{collection_id}/lifecycle" \
  -H "Authorization: Bearer $API_KEY" \
  -H "X-Namespace: ns_your_namespace_id"

Returns the current state plus counts from both the hot index and the object-storage backup, so you can verify a transition completed.

Transition a collection

curl -X PATCH "https://api.mixpeek.com/v1/collections/{collection_id}/lifecycle" \
  -H "Authorization: Bearer $API_KEY" \
  -H "X-Namespace: ns_your_namespace_id" \
  -H "Content-Type: application/json" \
  -d '{"lifecycle_state": "cold"}'

cold evicts the collection from the vector store (vectors preserved via object storage).
active (from cold) rehydrates: the backup is restored into the hot index.
archived permanently removes the vectors.

archived is irreversible for vectors. If there is any chance you’ll search this collection again, use cold. Restoring from cold is cheap; rebuilding from archive means re-processing the source objects.

When to use each tier

active: anything queried by production retrievers.
cold: completed campaigns and historical projects you want restorable on demand (for example, an annual review): rehydrate, query, and send back to cold.
archived: data you’re retaining for record-keeping where the documents’ metadata is enough and vector search will never be needed again.

Buckets & storage classes: tiering for the source objects (this page covers the index tier; the two are independent)
Manage data: document-level operations
Deduplication & re-processing: what happens if you do need to rebuild from source

​Lifecycle states

​Check lifecycle status

​Transition a collection

​When to use each tier

​Related

Lifecycle states

Check lifecycle status

Transition a collection

When to use each tier

Related