How do you retrieve a Qdrant point by ID, and how is that different from vector search?

A point lookup addresses a known ID directly, so it is conceptually similar to a primary-key read. It does not calculate similarity or rank neighboring vectors.

Vector search starts with a query vector and asks Qdrant to rank candidate points according to the configured distance metric. The caller may not know the IDs beforehand. That distinction matters because direct lookup is appropriate when an application already knows the entity, while vector search is appropriate for discovery by semantic similarity.

The trade-off is deterministic identity lookup versus relevance-based retrieval. A common architecture uses vector search to discover candidate IDs and then direct database or point lookups to fetch authoritative application state.

A common mistake is using vector search to retrieve a known document. That wastes search resources and introduces unnecessary relevance logic.