A team needs to locate similar incidents without known output labels. Which approach directly produces a reusable semantic representation for nearest-neighbor search?

1. Compute text embeddings for incident descriptions.
2. Train a classifier whose only class is incident.
3. Assign every incident the same fixed identifier.
4. Discard the description and retain only the upload timestamp.