Mental model
Vector search embeds a query into the same representation space as indexed items, then retrieves nearby candidates under a chosen similarity and index strategy.
Theory
Exact nearest-neighbor search compares every vector; approximate indexes trade some recall for speed and memory. Metadata filters enforce scope and reduce candidates. Index parameters affect build time, query latency, and recall. A retrieval evaluation needs known relevant items, not merely appealing demo results.
Alternatives and trade-offs
Lexical search excels at exact terms, vector search captures semantic similarity, hybrid search combines them, and reranking applies a more expensive relevance model to a shortlist.
Failure modes and misconceptions
Do not treat distance as relevance certainty, omit access filters, change embedding models without reindexing, or tune only on a few hand-picked questions.
Knowledge check
What does an approximate index trade for lower query latency?
Decision scenario
A legal system filters by matter and permission before vector search, measures recall at k, and retains exact search for citations and case identifiers.
Learning outcomes
- Explain Vector Search as a system mechanism rather than a slogan.
- Compare its alternatives, trade-offs, and production failure modes.
- Apply the concept to a decision and identify evidence that would validate it.
Trade-offs
Using Vector Search can improve capability or control, but it also introduces cost, latency, complexity, and failure modes that must be measured against an explicit objective.