Vector DB Search Tuning Cheat Sheet

Last Updated: November 21, 2025

Focus Areas

Control	Why it matters
`Distance metric`	Euclidean for dense, cosine for normalized embeddings.
`Dimension reduction`	PCA + quantization reduce storage and speed up queries.
`Filter clauses`	Apply metadata filters before vector scoring to reduce noise.
`Shards/replicas`	Scale queries by adjusting shard count and replication.


         index.search(query_vector, k=10, filter={'status': 'published'})

Combine semantic score with business metadata.


         client.create_index(name='docs', metric='cosine', dimension=1536)

Match your embedding dimension and metric to your model.


         index.update_config({'ef': 128, 'pq': 16})

Tune query accuracy vs latency for HNSW/IVF indexes.


         reranker(query, candidates)

Use a small cross-encoder to boost top hits.

Tuning vector search means balancing recall (smart filters + rerankers) and latency (shard configuration + index metrics). Monitor both.

💡 Pro Tip: Log recall + latency after each change so you can rollback noisy embeddings.