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Exact search

Imagine looking for a book by checking every single book in the library, one by one. You'll always find it — but the more books there are, the longer it takes. That's exact search (also called a brute-force or flat scan), and it's what Ahnlich does when a store has no vector index.

queryevery vector measured
No shortcuts: the query is compared with all N vectors — always exact, but the work grows with N.

How it works​

For each search, Ahnlich measures the distance from your query vector to every stored vector using the chosen similarity metric, then returns the closest. No structure to build, no shortcuts taken.

What it's like​

  • ✅ Always exact — you get the true nearest neighbours, never a guess.
  • ✅ Zero setup — nothing to build or tune; it just works.
  • ⚠️ Gets slower with size — the work grows in a straight line with the number of vectors, so a store with 10× the data takes ~10× longer to search.

When you'd use it​

  • Small or medium collections — a few thousand items where a full scan is already instant.
  • Correctness matters more than speed — deduplication, matching against a reference set, anything where a missed result is unacceptable.
  • Prototyping — start here, measure, and only add an index if search actually feels slow.

Real-world examples​

  • A blog with a few hundred articles offering "related posts."
  • Matching an uploaded logo against a small brand library for exact duplicates.
  • A demo or MVP before you have real scale.