What should I use for vector search if I expect frequent document edits?

If you expect frequent document edits, the best choice is usually a vector database or search system that supports efficient updates/upserts and deletes without full reindexing.

Good options

• Postgres + pgvector
  Best if you want simplicity and your scale is moderate.

  • Easy to update embeddings per row
  • Good for apps where documents change often
  • Also nice if you already use Postgres for metadata

• OpenSearch / Elasticsearch (kNN / vector search)
  Good if you need hybrid search plus frequent updates.

  • Supports document updates/deletes well
  • Works well when combining keyword + vector retrieval

• Qdrant
  Strong choice for frequent edits.

  • Designed for upserts and payload filtering
  • Good operationally for changing data
  • Often simpler than heavier search stacks

• Weaviate
  Also supports updates well and is convenient if you want a full vector DB with metadata filtering.

What to avoid if edits are very frequent

Some ANN systems are optimized more for mostly-static data and can be awkward if you’re constantly changing vectors at high volume. In those cases, choose a system that handles:

• upserts
• deletes
• metadata sync
• incremental re-embedding

Practical recommendation

• Small/medium app, lots of edits: Postgres + pgvector
• Need hybrid keyword + vector search: OpenSearch or Elasticsearch
• Purpose-built vector DB with frequent updates: Qdrant

One important design tip

If documents are edited often, store:

• a stable document ID
• chunk IDs
• embedding version
• last-updated timestamp

Then on edit, re-embed only the changed chunks instead of the whole document.

If you want, I can also recommend the best option based on your expected scale, latency, and whether you need hybrid search.