PluginPublic

testland/vector-search-recall-tests

Use when HNSW or IVF parameters are being tuned, an embedding model is swapped, or vector search recall was never measured. Builds brute-force ground truth, measures recall@k against latency and detects embedding drift for Qdrant, Weaviate, pgvector, Pinecone, Elasticsearch k-NN and Milvus.

Quality

91%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Low

Low-risk findings worth noting

ANN-Benchmarks cross-engine baseline

Clone-and-run detail for vector-search-recall-tests Step 6. Per the ANN-Benchmarks docs, the framework "evaluates 37+ ANN algorithms ... by plotting recall against queries per second across various datasets" including HNSW (multiple impls), FAISS IVF, ScaNN, Annoy, Qdrant, Weaviate, Milvus.

git clone https://github.com/erikbern/ann-benchmarks.git
cd ann-benchmarks
python install.py --algorithm hnswlib
python run.py --algorithm hnswlib --dataset glove-100-angular
python plot.py --dataset glove-100-angular

Outputs per-engine recall/QPS curves. Use to pick an engine + initial parameter set.

Note ANN-Benchmarks uses synthetic datasets (GloVe, SIFT, GIST); in-domain corpora behave differently, so treat the curves as a starting point, not a substitute for the in-product recall test in SKILL.md Steps 1-4.