landing_page/qdrant-landing/content/documentation/tutorials-search-engineering/retrieval-quality.md

title, aliases, weight

title	aliases	weight
Measuring ANN Precision	/documentation/tutorials/retrieval-quality/ /documentation/beginner-tutorials/retrieval-quality/	5

Measuring ANN Precision

Time: 15 min	Level: Beginner

This tutorial focuses on ANN precision: how closely approximate nearest-neighbor (ANN) search matches exact kNN search. To measure ANN precision, you compare Qdrant's approximate top-k against the exact kNN top-k using precision@k, then tune HNSW parameters to trade memory and build time for higher precision.

To learn more about retrieval quality evaluation, see the evaluation ladder.

Measure ANN Precision with the Web UI

Qdrant's Web UI has a Search Quality tab that measures the gap between approximate and exact search without requiring evaluation code. Open the dashboard at http://localhost:6333/dashboard (or your cluster's dashboard on Qdrant Cloud), navigate to your collection, open the Search Quality tab, and click Check Index Quality to run the comparison.

The tab reports average precision@k (1.0 = perfect overlap; 0.95+ is typical for well-tuned HNSW). HNSW has tunable parameters that trade memory and index build time for higher precision.

Tuning the HNSW Parameters

HNSW is a hierarchical graph where each node has a set of links to other nodes. The m parameter controls the number of edges per node: higher m means higher precision at the cost of more memory. The ef_construct parameter controls how many neighbors are considered during index building: higher ef_construct means higher precision at the cost of longer indexing time. Defaults are m=16 and ef_construct=100.

For the full list of HNSW parameters, including on-disk storage and precision/memory trade-offs, see Optimize Performance.

Toggle advanced mode in the Search Quality tab to tune these parameters inline. Raise m to 32 and ef_construct to 200, then run the evaluation again.

Precision should increase at the cost of higher build time and memory.

Tune until you hit the point that matches your quality and cost targets.

Automate in CI with Python

The Web UI is the fastest way to check precision interactively. For continuous integration or scripted regression tests, the Qdrant client exposes the same exact-search mode via search_params=models.SearchParams(exact=True). Compare the ANN and exact top-k sets yourself and compute precision@k.

This helper takes a list of query vectors and returns the average precision@k. Use a representative sample of query vectors from your workload as your test set.

from qdrant_client import QdrantClient, models


def avg_precision_at_k(
    client: QdrantClient,
    collection_name: str,
    test_vectors: list,
    k: int,
) -> float:
    precisions = []
    for vector in test_vectors:
        ann_ids = {
            p.id for p in client.query_points(
                collection_name=collection_name,
                query=vector,
                limit=k,
            ).points
        }
        knn_ids = {
            p.id for p in client.query_points(
                collection_name=collection_name,
                query=vector,
                limit=k,
                search_params=models.SearchParams(exact=True),
            ).points
        }
        precisions.append(len(ann_ids & knn_ids) / k)

    return sum(precisions) / len(precisions)

Wire it into CI and fail the job when precision falls below your target threshold. This catches regressions from embedding model swaps or index config changes before they reach production.

Wrapping Up

Measuring ANN precision keeps HNSW tuning honest. The Search Quality tab gives you a quick interactive read; the Python helper plugs into CI to catch regressions after embedding model changes or index config updates.

Once ANN precision is on target, the next layer is whether the retrieved results are relevant to users. See Building a Golden Query Set.