# Backups, Migrations and Re-Embedding — Vector Databases

Source: https://www.geekswithgeeks.com/en/vector-databases/s-backup

> Protect data, change models safely and recover from disasters.

## You can rebuild an index, not lost text

Decide what is the **source of truth**. The original documents and their metadata are irreplaceable; vectors and ANN indexes can be **rebuilt** (at a cost in time and embedding fees), so keep the sources safe and treat the index as a derived, rebuildable artefact, while still backing it up to shorten recovery. Use the engine's mechanism: `pg_dump`/base backups with point-in-time recovery for PostgreSQL, **snapshots** for dedicated engines, and **test restores** regularly. For a **new embedding model or dimension**, never mix vectors: create a **new collection or index**, re-embed everything, compare quality on the golden set, switch traffic (alias or config), and keep the old one for rollback. Version your schema and record the **model name, version and chunking settings** with the data. Plan **disaster recovery**: how long to restore, how much data you can afford to lose (RPO) and how long you can be down (RTO).

## A model-upgrade runbook

Steps for replacing the embedding model without breaking search.

```text
1. create collection/index "docs_v2" (new dimension / metric if the model changed)
2. re-embed all source text with the NEW model; write idempotently by deterministic id
3. build the ANN index; check counts and a recall sample vs exact search
4. run the golden-set evaluation on v1 and v2; compare recall@k and MRR
5. shadow traffic: send real queries to both, compare results and latency
6. switch the alias/config from docs_v1 to docs_v2 (instant, reversible)
7. keep docs_v1 for a rollback window, then delete it (and its backups, per retention policy)
```

## Practise a restore

A backup you have never restored is a hope, not a plan. Rehearse recovery and time it.

**Quiz:** How should you switch to a new embedding model?

- [ ] Replace the model and keep old vectors
- [ ] Mix old and new vectors in the same index
- [x] Build a new index, re-embed everything, compare, then switch with a rollback option
- [ ] Delete the sources

*Answer:* Build a new index, re-embed everything, compare, then switch with a rollback option. Different models produce incompatible spaces.