vector-search-recall-tests

Per the [ANN-Benchmarks docs] and [Qdrant search docs], tests must measure recall@k against known ground truth - engine self-eval doesn't catch index corruption or parameter drift.

When to use

• Production uses vector search for retrieval (RAG, semantic search, recommendations).
• HNSW / IVF / ScaNN parameter tuning - need quantitative test for the recall/latency Pareto curve.
• Embedding model upgrade - verify recall doesn't drop on the existing corpus.

Step 1 - Construct ground truth (brute-force k-NN)

Ground truth = top-k closest documents under exact (brute-force) distance. Compute once for a fixed query set: