Vector databases

Beginner — Why use a vector index?

• It makes repeated similarity queries efficient over a growing corpus.
• Small datasets can use exact comparison, but larger collections need indexing and careful metadata filtering for relevance and eligibility.

Intermediate — Exact versus approximate search?

• Exact search examines eligible vectors thoroughly; approximate search trades some nearest-neighbor recall for speed and scale.
• I benchmark with realistic filters, corpus sizes, and query patterns rather than assuming ANN is always required.

Senior — Dedicated vector store or existing database?

• I first test the existing database's vector capability against filtered recall, latency, lifecycle, security, and cost needs.
• A dedicated store is justified when it solves measured limitations enough to offset another operational dependency.

Scenario — Unauthorized documents are removed after top-k, leaving no results. What is wrong?

• The retrieval process selected candidates from an ineligible corpus.
• I move supported eligibility filters into retrieval and recheck object permissions before evidence reaches the model.
• Post-filtering alone can both underfill results and expose data upstream.